Repository: fvisin/reseg
Branch: master
Commit: 8040d190fcdb
Files: 13
Total size: 224.8 KB
Directory structure:
gitextract_ir9a6mvo/
├── .gitignore
├── LICENSE
├── README.md
├── camvid.py
├── config_datasets.py
├── evaluate_camvid.py
├── get_info_model.py
├── helper_dataset.py
├── layers.py
├── padded.py
├── reseg.py
├── utils.py
└── vgg16.py
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
*.pyc
segmentations
*_models/
tmp
evaluate*
*.pkl
!evaluate_camvid.py
================================================
FILE: LICENSE
================================================
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may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
.
================================================
FILE: README.md
================================================
If you use this code, please cite one of the following papers:
* \[1\] Francesco Visin, Kyle Kastner, Kyunghyun Cho, Matteo Matteucci, Aaron
Courville, Yoshua Bengio - [ReNet: A Recurrent Neural Network Based
Alternative to Convolutional Networks](
https://arxiv.org/pdf/1505.00393.pdf) ([BibTeX](
https://gist.github.com/fvisin/e450c4f55a527c5db802e69574b79a95#file-renet-bib))
* \[2\] Francesco Visin, Marco Ciccone, Adriana Romero, Kyle Kastner, Kyunghyun
Cho, Yoshua Bengio, Matteo Matteucci, Aaron Courville - [ReSeg: A Recurrent
Neural Network-based Model for Semantic Segmentation](
http://arxiv.org/pdf/1511.07053) ([BibTeX](
https://gist.github.com/fvisin/61b1dd3777ea91a0e3ad963366a61fb1#file-reseg-bib))
Setup
-----
#### Install Theano
Download Theano and make sure it's working properly. All the
information you need can be found by following this link:
http://deeplearning.net/software/theano/
#### Install other dependencies
This software relies on some amazing third-party software libraries.
You can install them with *pip*:
`pip install <--user> lasagne matplotlib Pillow progressbar2 pydot-ng retrying
scikit-image scikit-learn tabulate`
*(Use the `--user` option if you don't want to install them globally or you
don't have sudo privileges on your machine.)*
#### Download the CamVid dataset
Download the CamVid dataset from
http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/
Resize the images to 480X360 resolution. The program expects to find the
dataset data in `./datasets/camvid/`. You can change this path modifying
`camvid.py` if you want.
#### Download the VGG-16 weights
Download the VGG weights for Lasagne from:
https://s3.amazonaws.com/lasagne/recipes/pretrained/imagenet/vgg16.pkl
Once downloaded, rename them as `w_vgg16.pkl` and put them in the root
directory of this code.
Reproducing the Results
-----------------------
To reproduce the results of the ReSeg paper run `python evaluate_camvid.py` (or
`python evaluate_camvid_with_cb.py` to reproduce the experiment with class
balancing). Make sure to set the appropriate THEANO_FLAGS to run the model on
your machine (most probably `export THEANO_FLAGS=device=gpu,floatX=float32`)
The program will output some metrics on the current minibatch iteration during
training:
Epoch 0/5000 Up 367 Cost 270034.031250, DD 0.000046, UD 0.848205
(None, 360, 480, 3)
More in detail, it will show the current epoch, the incremental update counter
(i.e. number of minibatches seen), the cost of the current iteration, the time
(in seconds) required to load the data `DD` and to train and update the
network's parameters `DD`. Finally, it will print the size of the currently
processed minibatch. `None` will be displayed on variable-sized dimensions.
At the end of each epoch, it will validate the performances on the training,
validation and test set and save some sample images for each set in a
*segmentations* directory inside the root directory of the script.
At the end of the training `get_info_model.py` can be used to show some
information on the trained model. Run `python get_info_model.py -h` for a
list of the arguments and their explanation.
****
Note: In case you want to modify this code to reproduce the results of
"Combining the best of convolutional layers and recurrent layers: A hybrid
network for semantic segmentation" please let us know!
Acknowledgments
---------------
Many people contributed in different ways to this project. We are extremely
thankful to the [Theano](http://deeplearning.net/software/theano/) developers
and to many people at [MILA](http://mila.umontreal.ca/) for their support and
for the many insightful discussions. We also thank the developer of
[Lasagne](http://lasagne.readthedocs.io/), a powerful yet light framework on top of
Theano. I wish I discovered it at the beginning of this project! :)
Finally, our gratitude goes to the developers of all the great libraries we
used in this project, to all the people who got involved with the project at
any level and to our generous sponsors.
================================================
FILE: camvid.py
================================================
from __future__ import division
import os
from collections import OrderedDict
import numpy as np
from skimage import img_as_ubyte
from skimage.color import label2rgb, rgb2hsv
from skimage.io import ImageCollection
from skimage.transform import resize
from itertools import izip
from config_datasets import (colormap_datasets as colors_list)
from helper_dataset import convert_RGB_mask_to_index, save_image
N_DEBUG = -5
DEBUG_SAVE_IMG = False
DEBUG_SAVE_MASK = False
intX = 'uint8'
def properties():
return { # 'reshape': [212, 264, 3],
# 'reorder': [0, 1, 2],
# 'rereorder': [0, 1, 2]
'has_void_class': True
}
"""
compare_mask_image_filenames:
mask = [i.split('/')[-1].replace('_L.png', '.png') for i in filenames_mask]
compare_mask_image_filenames_segnet
mask = [i.split('/')[-1].replace('annot', '') for i in filenames_mask]
"""
def load_images(img_path, gt_path, colors, load_greylevel_mask=False,
resize_images=False, resize_size=-1, save=False,
color_space='RGB'):
if load_greylevel_mask:
assert not save
images = []
masks = []
filenames_images = []
print "Loading images..."
# print img_path
labs = ImageCollection(os.path.join(img_path, "*.png"))
for i, (inpath, im) in enumerate(izip(labs.files, labs)):
if i == N_DEBUG:
break
assert np.amax(im) <= 255, "Image is not 8-bit"
if resize_images and resize_size != -1:
w, h = resize_size
im = resize(im, (h, w), order=3)
# order=3 : bicubic interpolation
# it's normalized by default btw 0-1 by the resize function
# so we want to preserve the range
im = img_as_ubyte(im)
im = im.astype(intX)
if color_space == "HSV":
im = rgb2hsv(im)
if DEBUG_SAVE_IMG:
outpath = inpath.replace('imgs', 'debug_imgs')
save_image(outpath, im)
images.append(im)
filenames_images.append(inpath)
print "Loading masks..."
if load_greylevel_mask:
gt_path = gt_path.replace("gt", "gt_grey")
filenames_mask = []
labs = ImageCollection(os.path.join(gt_path, "*.png"))
for i, (inpath, im) in enumerate(izip(labs.files, labs)):
if i == N_DEBUG:
break
if resize_images and resize_size != -1:
w, h = resize_size
im = (resize(im, (h, w), order=0) * 255).astype(np.uint8)
filenames_mask.append(inpath)
# print inpath
if load_greylevel_mask:
mask = im
else:
mask = convert_RGB_mask_to_index(
im, colors, ignore_missing_labels=True)
if save:
outpath = inpath.replace("gt", "gt_grey")
save_image(outpath, mask)
mask = np.array(mask).astype(intX)
if DEBUG_SAVE_MASK:
outpath = inpath.replace('gt', 'debug_gt')
outpath = inpath.replace('annot', 'debug_annot')
# print np.unique(mask)
save_image(outpath, label2rgb(mask, colors=colors_list['camvid']))
masks.append(mask)
assert len(images) == len(
masks), "Train Images and masks are not in the same quantity"
return images, masks, filenames_images
def load_dataset_camvid(path, load_greylevel_mask=False, classes='subset_11',
resize_images=False,
resize_size=-1,
use_standard_split=True,
save=False,
color_space='RGB'):
# WORKING: but image Seq05VD_f02610_L.png has some problems, some pixels
# have other values so I treated as Void
img_train_path = os.path.join(path, 'imgs', 'train')
img_test_path = os.path.join(path, 'imgs', 'test')
img_val_path = os.path.join(path, 'imgs', 'val')
gt_train_path = os.path.join(path, 'gt', 'train')
gt_test_path = os.path.join(path, 'gt', 'test')
gt_val_path = os.path.join(path, 'gt', 'val')
camvid_all_colors = OrderedDict([
("Animal", np.array([[64, 128, 64]], dtype=np.uint8)),
("Archway", np.array([[192, 0, 128]], dtype=np.uint8)),
("Bicyclist", np.array([[0, 128, 192]], dtype=np.uint8)),
("Bridge", np.array([[0, 128, 64]], dtype=np.uint8)),
("Building", np.array([[128, 0, 0]], dtype=np.uint8)),
("Car", np.array([[64, 0, 128]], dtype=np.uint8)),
("CartLuggagePram", np.array([[64, 0, 192]], dtype=np.uint8)),
("Child", np.array([[192, 128, 64]], dtype=np.uint8)),
("Column_Pole", np.array([[192, 192, 128]], dtype=np.uint8)),
("Fence", np.array([[64, 64, 128]], dtype=np.uint8)),
("LaneMkgsDriv", np.array([[128, 0, 192]], dtype=np.uint8)),
("LaneMkgsNonDriv", np.array([[192, 0, 64]], dtype=np.uint8)),
("Misc_Text", np.array([[128, 128, 64]], dtype=np.uint8)),
("MotorcycleScooter", np.array([[192, 0, 192]], dtype=np.uint8)),
("OtherMoving", np.array([[128, 64, 64]], dtype=np.uint8)),
("ParkingBlock", np.array([[64, 192, 128]], dtype=np.uint8)),
("Pedestrian", np.array([[64, 64, 0]], dtype=np.uint8)),
("Road", np.array([[128, 64, 128]], dtype=np.uint8)),
("RoadShoulder", np.array([[128, 128, 192]], dtype=np.uint8)),
("Sidewalk", np.array([[0, 0, 192]], dtype=np.uint8)),
("SignSymbol", np.array([[192, 128, 128]], dtype=np.uint8)),
("Sky", np.array([[128, 128, 128]], dtype=np.uint8)),
("SUVPickupTruck", np.array([[64, 128, 192]], dtype=np.uint8)),
("TrafficCone", np.array([[0, 0, 64]], dtype=np.uint8)),
("TrafficLight", np.array([[0, 64, 64]], dtype=np.uint8)),
("Train", np.array([[192, 64, 128]], dtype=np.uint8)),
("Tree", np.array([[128, 128, 0]], dtype=np.uint8)),
("Truck_Bus", np.array([[192, 128, 192]], dtype=np.uint8)),
("Tunnel", np.array([[64, 0, 64]], dtype=np.uint8)),
("VegetationMisc", np.array([[192, 192, 0]], dtype=np.uint8)),
("Wall", np.array([[64, 192, 0]], dtype=np.uint8)),
("Void", np.array([[0, 0, 0]], dtype=np.uint8))
])
camvid_11_colors = OrderedDict([
("Sky", np.array([[128, 128, 128]], dtype=np.uint8)),
("Building", np.array([[128, 0, 0], # Building
[64, 192, 0], # Wall
[0, 128, 64] # Bridge
], dtype=np.uint8)),
("Column_Pole", np.array([[192, 192, 128]], dtype=np.uint8)),
("Road", np.array([[128, 64, 128], # Road
[128, 0, 192], # LaneMkgsDriv
[192, 0, 64], # LaneMkgsNonDriv
[128, 128, 192] # RoadShoulder
], dtype=np.uint8)),
("Sidewalk", np.array([[0, 0, 192], # Sidewalk
[64, 192, 128] # ParkingBlock
], dtype=np.uint8)),
("Tree", np.array([[128, 128, 0], # Tree
[192, 192, 0] # VegetationMisc
], dtype=np.uint8)),
("SignSymbol", np.array([[192, 128, 128], # SignSymbol
# [128, 128, 64], # Misc_Text
[0, 64, 64], # TrafficLight
[0, 0, 64] # TrafficCone
], dtype=np.uint8)),
("Fence", np.array([[64, 64, 128]], dtype=np.uint8)),
("Car", np.array([[64, 0, 128], # Car
[192, 128, 192], # Truck_Bus
[64, 128, 192], # SUVPickupTruck
[128, 64, 64], # OtherMoving
[64, 0, 192], # CartLuggagePram
], dtype=np.uint8)),
("Pedestrian", np.array([[64, 64, 0], # Pedestrian
[192, 128, 64] # Child
], dtype=np.uint8)),
("Bicyclist", np.array([[0, 128, 192], # Bicyclist
[192, 0, 192], # MotorcycleScooter
], dtype=np.uint8)),
("Void", np.array([[0, 0, 0]], dtype=np.uint8))
]) # consider as void all the other classes
camvid_colors = camvid_11_colors if classes == 'subset_11' else \
camvid_all_colors
print "Processing Camvid train dataset..."
img_train, mask_train, filenames_train = load_images(
img_train_path, gt_train_path, camvid_colors, load_greylevel_mask,
resize_images, resize_size, save, color_space)
print "Processing Camvid test dataset..."
img_test, mask_test, filenames_test = load_images(
img_test_path, gt_test_path, camvid_colors, load_greylevel_mask,
resize_images, resize_size, save, color_space)
print "Processing Camvid validation dataset..."
img_val, mask_val, filenames_val = load_images(
img_val_path, gt_val_path, camvid_colors, load_greylevel_mask,
resize_images, resize_size, save, color_space)
return (img_train, mask_train, filenames_train,
img_test, mask_test, filenames_test,
img_val, mask_val, filenames_val)
def load_dataset_camvid_segnet(path):
img_train_path = os.path.join(path, 'train')
img_valid_path = os.path.join(path, 'val')
img_test_path = os.path.join(path, 'test')
gt_train_path = os.path.join(path, 'trainannot')
gt_valid_path = os.path.join(path, 'valannot')
gt_test_path = os.path.join(path, 'testannot')
camvid_colors = OrderedDict([
("Sky", np.array([128, 128, 128], dtype=np.uint8)),
("Building", np.array([128, 0, 0], dtype=np.uint8)),
("Column_Pole", np.array([192, 192, 128], dtype=np.uint8)),
("Road", np.array([128, 64, 128], dtype=np.uint8)),
("Sidewalk", np.array([0, 0, 192], dtype=np.uint8)),
("Tree", np.array([128, 128, 0], dtype=np.uint8)),
("SignSymbol", np.array([192, 128, 128], dtype=np.uint8)),
("Fence", np.array([64, 64, 128], dtype=np.uint8)),
("Car", np.array([64, 0, 128], dtype=np.uint8)),
("Pedestrian", np.array([64, 64, 0], dtype=np.uint8)),
("Bicyclist", np.array([0, 128, 192], dtype=np.uint8)),
("Void", np.array([0, 0, 0], dtype=np.uint8))
])
print "Processing Camvid SegNet train dataset..."
img_train, mask_train, filenames_train = load_images(
img_train_path, gt_train_path, camvid_colors, load_greylevel_mask=True,
save=False) # load_greylevel_mask=True by default because it's grey
print "Processing Camvid SegNet valid dataset..."
img_valid, mask_valid, filenames_valid = load_images(
img_valid_path, gt_valid_path, camvid_colors, load_greylevel_mask=True,
save=False) # load_greylevel_mask=True by default because it's grey
print "Processing Camvid SegNet test dataset..."
img_test, mask_test, filenames_test = load_images(
img_test_path, gt_test_path, camvid_colors, load_greylevel_mask=True,
save=False) # load_greylevel_mask=True by default because it's grey
return (img_train, mask_train, filenames_train,
img_test, mask_test, filenames_test,
img_valid, mask_valid, filenames_valid)
def load_data(
path=os.path.expanduser('./datasets/camvid/'),
randomize=False,
resize_images=True,
resize_size=[320, 240], # w x h : 960x720, 480x360, 320x240
color=False,
color_space='RGB',
normalize=False,
classes='subset_11', # subset_11 , all
version='segnet', # standard, segnet
split=[.44, .22],
with_filenames=False,
load_greylevel_mask=False,
save=False,
compute_stats='all',
rng=None,
with_fullmasks=False,
**kwargs
):
"""Dataset loader
Parameter
---------
path : string the path to the dataset images.
randomize False
resize False
use_fullsize_images True
version: string
standard, segnet
compute_stas: string
train, all
"""
#############
# LOAD DATA #
#############
if version == 'segnet':
path = os.path.join(path, 'segnet')
(img_train_segnet,
mask_train_segnet,
filenames_train_segnet,
img_test,
mask_test,
filenames_test,
img_val_segnet,
mask_val_segnet,
filenames_val_segnet) = load_dataset_camvid_segnet(path)
img_train = img_train_segnet
mask_train = mask_train_segnet
filenames_train = filenames_train_segnet
img_val = img_val_segnet
mask_val = mask_val_segnet
filenames_val = filenames_val_segnet
elif version == 'standard':
path = os.path.join(path, 'splitted_960x720')
(img_train,
mask_train,
filenames_train,
img_test,
mask_test,
filenames_test,
img_val,
mask_val,
filenames_val) = load_dataset_camvid(
path, resize_images=resize_images, resize_size=resize_size,
load_greylevel_mask=load_greylevel_mask, classes=classes,
save=save, color_space=color_space)
# if compute_stats == 'all':
# images = np.asarray(img_train + img_val + img_test)
# elif compute_stats == 'train':
# images = np.asarray(img_train)
# all images have the same dimension --> we can compute perpixel statistics
# mean = images.mean(axis=0)[np.newaxis, ...]
# std = np.maximum(images.std(axis=0), 1e-8)[np.newaxis, ...]
# print "Computing dataset statistics ..."
mean = 0
std = 0
# split datasets
ntrain = len(img_train)
ntest = len(img_test)
nvalid = len(img_val)
ntot = ntrain + ntest + nvalid
train_set_x = np.array(img_train)
train_set_y = np.array(mask_train)
test_set_x = np.array(img_test)
test_set_y = np.array(mask_test)
valid_set_x = np.array(img_val)
valid_set_y = np.array(mask_val)
# u_train, c_train = np.unique(train_set_y, return_counts=True)
# u_valid, c_valid = np.unique(valid_set_y, return_counts=True)
# u_test, c_test = np.unique(test_set_y, return_counts=True)
#
# print u_train
# print np.round(100 * c_train / np.sum(c_train), 2)
#
# print u_valid
# print np.round(100 * c_valid / np.sum(c_valid), 2)
#
# print u_test
# print np.round(100 * c_test / np.sum(c_test), 2)
train = (train_set_x, train_set_y)
valid = (valid_set_x, valid_set_y)
test = (test_set_x, test_set_y)
filenames = [np.array(filenames_train),
np.array(filenames_val),
np.array(filenames_test)]
print "load_data Done!"
print('Tot images:{} Train:{} Valid:{} Test:{}').format(
ntot, ntrain, nvalid, ntest)
"""
# Debug for types
print (train_set_x.dtype)
print (test_set_x.dtype)
print (valid_set_x.dtype)
print (train_set_y.dtype)
print (test_set_y.dtype)
print (valid_set_y.dtype)
print (train_set_x[0].dtype)
print (test_set_x[0].dtype)
print (valid_set_x[0].dtype)
print (train_set_y[0].dtype)
print (test_set_y[0].dtype)
print (valid_set_y[0].dtype)
"""
out_list = [train, valid, test, mean, std]
if with_filenames:
out_list.append(filenames)
if with_fullmasks:
out_list.append([])
return out_list
if __name__ == '__main__':
load_data(save=False)
================================================
FILE: config_datasets.py
================================================
from collections import OrderedDict
import numpy as np
# COLORMAPS
cmaps = [('Perceptually Uniform Sequential',
['viridis', 'inferno', 'plasma', 'magma']),
('Sequential', ['Blues', 'BuGn', 'BuPu',
'GnBu', 'Greens', 'Greys', 'Oranges', 'OrRd',
'PuBu', 'PuBuGn', 'PuRd', 'Purples', 'RdPu',
'Reds', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd']),
('Sequential (2)', ['afmhot', 'autumn', 'bone', 'cool',
'copper', 'gist_heat', 'gray', 'hot',
'pink', 'spring', 'summer', 'winter']),
('Diverging', ['BrBG', 'bwr', 'coolwarm', 'PiYG', 'PRGn', 'PuOr',
'RdBu', 'RdGy', 'RdYlBu', 'RdYlGn', 'Spectral',
'seismic']),
('Qualitative', ['Accent', 'Dark2', 'Paired', 'Pastel1',
'Pastel2', 'Set1', 'Set2', 'Set3']),
('Miscellaneous', ['gist_earth', 'terrain', 'ocean', 'gist_stern',
'brg', 'CMRmap', 'cubehelix',
'gnuplot', 'gnuplot2', 'gist_ncar',
'nipy_spectral', 'jet', 'rainbow',
'gist_rainbow', 'hsv', 'flag', 'prism'])]
# ##### CAMVID ##### #
colormap_camvid = OrderedDict([
(0, np.array([128, 128, 128], dtype=np.uint8)), # sky
(1, np.array([128, 0, 0], dtype=np.uint8)), # Building
(2, np.array([192, 192, 128], dtype=np.uint8)), # Pole
(3, np.array([128, 64, 128], dtype=np.uint8)), # Road
(4, np.array([0, 0, 192], dtype=np.uint8)), # Sidewalk
(5, np.array([128, 128, 0], dtype=np.uint8)), # Tree
(6, np.array([192, 128, 128], dtype=np.uint8)), # SignSymbol
(7, np.array([64, 64, 128], dtype=np.uint8)), # Fence
(8, np.array([64, 0, 128], dtype=np.uint8)), # Car
(9, np.array([64, 64, 0], dtype=np.uint8)), # Pedestrian
(10, np.array([0, 128, 192], dtype=np.uint8)), # Bicyclist
(11, np.array([0, 0, 0], dtype=np.uint8)) # Unlabeled
])
headers_camvid = ["Sky", "Building", "Column_Pole", "Road", "Sidewalk",
"Tree", "SignSymbol", "Fence", "Car", "Pedestrian",
"Bicyclist", "Void"]
# DATASET DICTIONARIES #
colormap_datasets = dict()
colormap_datasets["camvid"] = colormap_camvid
for key, value in colormap_datasets.iteritems():
colormap_datasets[key] = np.asarray(
[z for z in zip(*value.items())[1]]) / 255.
headers_datasets = dict()
headers_datasets["camvid"] = headers_camvid
================================================
FILE: evaluate_camvid.py
================================================
from reseg import train
import lasagne
def main(job_id, params):
result = train(
saveto=params['saveto'],
tmp_saveto=params['tmp-saveto'],
# Input Conv layers
in_nfilters=params['in-nfilters'],
in_filters_size=params['in-filters-size'],
in_filters_stride=params['in-filters-stride'],
in_W_init=params['in-W-init'],
in_b_init=params['in-b-init'],
in_nonlinearity=params['in-nonlinearity'],
# RNNs layers
dim_proj=params['dim-proj'],
pwidth=params['pwidth'],
pheight=params['pheight'],
stack_sublayers=params['stack-sublayers'],
RecurrentNet=params['RecurrentNet'],
nonlinearity=params['nonlinearity'],
hid_init=params['hid-init'],
grad_clipping=params['grad-clipping'],
precompute_input=params['precompute-input'],
mask_input=params['mask-input'],
# GRU specific params
gru_resetgate=params['gru-resetgate'],
gru_updategate=params['gru-updategate'],
gru_hidden_update=params['gru-hidden-update'],
gru_hid_init=params['gru-hid-init'],
# LSTM specific params
lstm_ingate=params['lstm-ingate'],
lstm_forgetgate=params['lstm-forgetgate'],
lstm_cell=params['lstm-cell'],
lstm_outgate=params['lstm-outgate'],
# RNN specific params
rnn_W_in_to_hid=params['rnn-W-in-to-hid'],
rnn_W_hid_to_hid=params['rnn-W-hid-to-hid'],
rnn_b=params['rnn-b'],
# Output upsampling layers
out_upsampling=params['out-upsampling'],
out_nfilters=params['out-nfilters'],
out_filters_size=params['out-filters-size'],
out_filters_stride=params['out-filters-stride'],
out_W_init=params['out-W-init'],
out_b_init=params['out-b-init'],
out_nonlinearity=params['out-nonlinearity'],
# Prediction, Softmax
intermediate_pred=params['intermediate-pred'],
class_balance=params['class-balance'],
# Special layers
batch_norm=params['batch-norm'],
use_dropout=params['use-dropout'],
dropout_rate=params['dropout-rate'],
use_dropout_x=params['use-dropout-x'],
dropout_x_rate=params['dropout-x-rate'],
# Optimization method
optimizer=params['optimizer'],
learning_rate=params['learning-rate'],
momentum=params['momentum'],
rho=params['rho'],
beta1=params['beta1'],
beta2=params['beta2'],
epsilon=params['epsilon'],
weight_decay=params['weight-decay'],
weight_noise=params['weight-noise'],
# Early stopping
patience=params['patience'],
max_epochs=params['max-epochs'],
min_epochs=params['min-epochs'],
# Sampling and validation params
validFreq=params['validFreq'],
saveFreq=params['saveFreq'],
n_save=params['n-save'],
# Batch params
batch_size=params['batch-size'],
valid_batch_size=params['valid-batch-size'],
shuffle=params['shuffle'],
# Dataset
dataset=params['dataset'],
color_space=params['color-space'],
color=params['color'],
resize_images=params['resize-images'],
resize_size=params['resize-size'],
# Pre_processing
preprocess_type=params['preprocess-type'],
patch_size=params['patch-size'],
max_patches=params['max-patches'],
# Data augmentation
do_random_flip=params['do-random-flip'],
do_random_shift=params['do-random-shift'],
do_random_invert_color=params['do-random-invert-color'],
shift_pixels=params['shift-pixels'],
reload_=params['reload']
# fixed params
)
return result
if __name__ == '__main__':
dataset = 'camvid'
path = dataset + '_models/model_recseg' + __file__[8:-3] + '.npz'
main(1, {
'saveto': path,
'tmp-saveto': 'tmp/' + path,
# Note: with linear_conv you cannot select every filter size.
# It is not trivial to invert with expand unless they are a
# multiple of the image size, i.e., you would have to "blend" together
# multiple predictions because one pixel cannot be fully predicted just
# by one element of the last feature map
# call ConvNet.compute_reasonable_values() to find these
# note you should pick one pair (p1, p2) from the first list and
# another pair (p3, p4) from the second, then set in_filter_size
# to be (p1, p3),(p2, p4)
# valid: 1 + (input_dim - filter_dim) / stride_dim
# Input Conv layers
'in-nfilters': 'conv3_3', # None = no input convolution
'in-filters-size': (),
'in-filters-stride': (),
'in-W-init': lasagne.init.GlorotUniform(),
'in-b-init': lasagne.init.Constant(0.),
'in-nonlinearity': lasagne.nonlinearities.rectify,
# RNNs layers
'dim-proj': [100, 100],
'pwidth': [1, 1],
'pheight': [1, 1],
'stack-sublayers': (True, True),
'RecurrentNet': lasagne.layers.GRULayer,
'nonlinearity': lasagne.nonlinearities.rectify,
'hid-init': lasagne.init.Constant(0.),
'grad-clipping': 0,
'precompute-input': True,
'mask-input': None,
# GRU specific params
'gru-resetgate': lasagne.layers.Gate(W_cell=None),
'gru-updategate': lasagne.layers.Gate(W_cell=None),
'gru-hidden-update': lasagne.layers.Gate(
W_cell=None,
nonlinearity=lasagne.nonlinearities.tanh),
'gru-hid-init': lasagne.init.Constant(0.),
# LSTM specific params
'lstm-ingate': lasagne.layers.Gate(),
'lstm-forgetgate': lasagne.layers.Gate(),
'lstm-cell': lasagne.layers.Gate(
W_cell=None,
nonlinearity=lasagne.nonlinearities.tanh),
'lstm-outgate': lasagne.layers.Gate(),
# RNN specific params
'rnn-W-in-to-hid': lasagne.init.Uniform(),
'rnn-W-hid-to-hid': lasagne.init.Uniform(),
'rnn-b': lasagne.init.Constant(0.),
# Output upsampling layers
'out-upsampling': 'grad',
'out-nfilters': [50, 50],
'out-filters-size': [(2, 2), (2, 2)],
'out-filters-stride': [(2, 2), (2, 2)],
'out-W-init': lasagne.init.GlorotUniform(),
'out-b-init': lasagne.init.Constant(0.),
'out-nonlinearity': lasagne.nonlinearities.rectify,
# Prediction, Softmax
'intermediate-pred': None,
'class-balance': None,
# Special layers
'batch-norm': False,
'use-dropout': False,
'dropout-rate': 0.5,
'use-dropout-x': False,
'dropout-x-rate': 0.8,
# Optimization method
'optimizer': lasagne.updates.adadelta,
'learning-rate': None,
'momentum': None,
'rho': None,
'beta1': None,
'beta2': None,
'epsilon': None,
'weight-decay': 0., # l2 reg
'weight-noise': 0.,
# Early stopping
'patience': 500, # Num updates with no improvement before early stop
'max-epochs': 5000,
'min-epochs': 100,
# Sampling and validation params
'validFreq': -1,
'saveFreq': -1, # Parameters pickle frequency
'n-save': -1, # If n-save is a list of indexes, the corresponding
# elements of each split are saved. If n-save is an
# integer, n-save random elements for each split are
# saved. If n-save is -1, all the dataset is saved
# Batch params
'batch-size': 5,
'valid-batch-size': 5,
'shuffle': True,
# Dataset
'dataset': dataset,
'color-space': 'RGB',
'color': True,
'resize-images': True,
'resize-size': (360, 480),
# Pre-processing
'preprocess-type': None,
'patch-size': (9, 9),
'max-patches': 1e5,
# Data augmentation
'do-random-flip': False,
'do-random-shift': False,
'do-random-invert-color': False,
'shift-pixels': 2,
'reload': False
})
================================================
FILE: get_info_model.py
================================================
import argparse
import collections
import cPickle as pkl
import matplotlib.pyplot as plt
import numpy
from tabulate import tabulate
from config_datasets import headers_datasets
def print_pkl_params(pkl_path, *args):
"""Loads a parameter pkl archive and prints the parameters
Parameters
----------
pkl_path : string
The path of the .pkl parameter archive.
*args : dict
The arguments to print_params.
"""
try:
options = pkl.load(open(pkl_path, 'rb'))
except IOError:
print "Couldn't load " + pkl_path
return 0
save_plot_path = pkl_path.replace('models', 'plots').replace('.npz.pkl',
'.pdf')
return print_params(options, save_plot_path, *args)
def print_params(fp, save_plot_path='', print_commit_hash=False, plot=False,
print_history=False, print_best_class_accuracy=False,
):
"""Prints the parameter of the model
Parameters
----------
fp : dict
The dictionary of the model's parameters
print_commit_hash : bool
If True, the commit hash will be printed
plot : bool
If True, the error curves will be plotted
print_history : bool
If True the history of the accuracies will be printed
"""
dataset = fp.get("dataset", "camvid")
errs = fp.get('history_acc', None)
if errs is None:
errs = fp.get('history_errs', None)
conf_matrices = numpy.array(fp['history_conf_matrix'])
iou_indeces = numpy.array(fp['history_iou_index'])
#nclasses = conf_matrices.shape[2] if len(conf_matrices) > 0 else -1
# hack for nyu because now I don't have the time to think to something else
# if dataset == 'nyu_depth':
# dataset = 'nyu_depth40' if nclasses == 41 else 'nyu_depth04'
headers = headers_datasets.get(dataset, None)
if headers is None:
headers = [str(i) for i in range(0, fp['out_nfilters'][-1])]
# they're already accuracies
if len(errs):
G_valid_idx = 3
C_valid_idx = 4
iou_valid_idx = 5
min_valid = numpy.argmax(errs[:, iou_valid_idx])
best = errs[min_valid]
if 'cityscapes' in dataset:
# for cityscapes we need to print the best iou index of the
# validation set (we don't have the test)
best_test_class_acc = numpy.round(iou_indeces[min_valid][1], 3)
else:
# in general we need to print the best accuracies of the test
# given by the best validation model
best_test_class_acc = numpy.round(
numpy.diagonal(conf_matrices[min_valid][2]) /
conf_matrices[min_valid][2].sum(axis=1), 3)
if len(best_test_class_acc) > 0 and print_best_class_accuracy:
best_per_class_accuracy = "|".join(
best_test_class_acc.astype('str'))
else:
best_per_class_accuracy = ''
# best_test_iou_indeces = numpy.round(iou_indeces[min_valid][2], 3)
if len(best) == 2:
error = (" ", round(best[0], 3), round(best[3], 3))
else:
if 'cityscapes' in dataset:
# print the validation errors
error = (round(best[0], 3), round(best[3], 3),
round(best[6], 3), round(best[4], 3),
round(best[5], 3))
else:
# print the test errors
error = (round(best[0], 3), round(best[3], 3),
round(best[6], 3), round(best[7], 3),
round(best[8], 3))
else:
error = [' ', ' ', ' ', ' ', ' ']
best_per_class_accuracy = ''
if 'history_unoptimized_cost' in fp:
huc = fp['history_unoptimized_cost']
else:
huc = None
# GRU specific fp
rnn_params = ' '
if fp['RecurrentNet'].__name__ == 'GRULayer':
rnn_params = ' '.join((fp['gru_resetgate'].__class__.__name__,
fp['gru_updategate'].__class__.__name__,
fp['gru_hidden_update'].__class__.__name__,
fp['gru_hid_init'].__class__.__name__,
str(fp['gru_hid_init'].val)))
# LSTM specific fp
if fp['RecurrentNet'].__name__ == 'LSTMLayer':
rnn_params = ' '.join((fp['lstm_ingate'].__class__.__name__,
fp['lstm_forgetgate'].__class__.__name__,
fp['lstm_cell'].__class__.__name__,
fp['lstm_outgate'].__class__.__name__))
# RNN specific fp
if fp['RecurrentNet'].__name__ == 'RNNLayer':
rnn_params = ' '.join((fp['rnn_W_hid_to_hid'].__class__.__name__,
fp['rnn_W_in_to_hid'].__class__.__name__,
fp['rnn_b'].__class__.__name__,
str(fp['rnn_b'].val)))
print("{0}|{1}|{2}|{3}|{4}|{5}|{6}|{7}|{8}|{9}|{10}|{11}|{12}|{13}|"
"{14}|{15}|{16}|{17}|{18}|{19}|{20}|{21}|{22}|{23}|{24}|{25}|"
"{26}|{27}|{28}|{29}|{30}|{31}|{32}|{33}|{34}|{35}|{36}|{37}|"
"{38}|{39}|{40}|{41}|{42}|{43}|{44}|{45}|{46}|{47}|{48}|{49}|"
"{50}|{51}|{52}|"
).format(
# Batch fp
fp['batch_size'],
# Dataset
fp['color'],
fp['color_space'],
fp.get('use_depth', ' '),
fp['shuffle'],
# Pre_processing
fp['preprocess_type'],
str(fp['patch_size']) + ' ' +
str(fp['max_patches']) if fp['preprocess_type'] in ('conv-zca',
'sub-lcn',
'subdiv-lcn',
'local_mean_sub')
else ' ',
fp['resize_images'],
fp['resize_size'],
# Data augmentation
fp['do_random_flip'],
fp['do_random_shift'],
fp['do_random_invert_color'],
# Input Conv layers
fp['in_vgg_layer'] if 'in_vgg_layer' in fp else fp['in_nfilters'],
fp['in_filters_size'] if isinstance(fp['in_nfilters'],
collections.Iterable) else ' ',
fp['in_filters_stride'] if isinstance(fp['in_nfilters'],
collections.Iterable) else ' ',
fp['in_W_init'].__class__.__name__ + ' , ' +
fp['in_b_init'].__class__.__name__ + ' ' + str(fp['in_b_init'].val)
if isinstance(fp['in_nfilters'], collections.Iterable) else ' ',
fp['in_nonlinearity'].__name__
if isinstance(fp['in_nfilters'], collections.Iterable) else ' ',
# RNNs layers
fp['dim_proj'],
(fp['pwidth'], fp['pheight']),
fp['stack_sublayers'],
fp['RecurrentNet'].__name__,
fp['nonlinearity'].__name__
if fp['RecurrentNet'].__name__ in ('LSTMLayer', 'RNNLayer') else ' ',
fp['hid_init'].__class__.__name__ + ' ' + str(fp['hid_init'].val),
fp['grad_clipping'],
# fp['precompute_input'],
# fp['mask_input'],
rnn_params,
# Output upsampling layers
fp['out_upsampling'],
fp['out_nfilters'] if fp['out_upsampling'] == 'grad' else ' ',
fp['out_filters_size'] if fp['out_upsampling'] == 'grad' else ' ',
fp['out_filters_stride'] if fp['out_upsampling'] == 'grad' else ' ',
fp['out_W_init'].__class__.__name__ + ', ' +
fp['out_b_init'].__class__.__name__ + ' ' + str(fp['out_b_init'].val),
fp['out_nonlinearity'].__name__ if fp['out_upsampling'] != 'linear'
else ' ',
# Prediction, Softmax
fp['intermediate_pred'],
fp['class_balance'],
# Special layers
fp['batch_norm'],
fp['use_dropout'],
fp['dropout_rate'] if fp['use_dropout'] else ' ',
fp['use_dropout_x'],
fp['dropout_x_rate'] if fp['use_dropout_x'] else ' ',
# Optimization method
fp['optimizer'].__name__,
fp.get('learning_rate', ' '),
','.join((str(fp.get('momentum', ' ')),
str(fp.get('beta1', ' ')),
str(fp.get('beta2', ' ')),
str(fp.get('epsilon', ' '))
)),
fp['weight_decay'],
fp['weight_noise'],
# Early stopping
fp['patience'],
fp['max_epochs'],
fp['min_epochs'],
len(errs),
error[0],
error[1],
error[2],
error[3],
error[4],
best_per_class_accuracy
)
if 'recseg_git_commit' in fp and print_commit_hash:
print("Recseg commit: %s" % fp['recseg_git_commit'])
if 'recseg_version' in fp and print_commit_hash:
print("Recseg commit: %s" % fp['recseg_version'])
if 'lasagne_version' in fp and print_commit_hash:
print("Lasagne commit: %s" % fp['lasagne_version'])
if 'theano_version' in fp and print_commit_hash:
print("theano commit: %s" % fp['theano_version'])
# plot error curves
if plot:
if errs.shape[1] == 2:
newerrs = numpy.zeros([errs.shape[0], errs.shape[1]+1])
newerrs[:, 1:3] = errs
errs = newerrs
# plt.subplot(2 if huc is not None else 1, 1, 1)
# Plot Global Pixels % error
plt.subplot(3, 1, 1)
plt_range = range(len(errs))
plt.plot(plt_range, 1 - errs[:, 0], label='train')
plt.plot(plt_range, 1 - errs[:, 3], label='valid')
plt.plot(plt_range, 1 - errs[:, 6], label='test')
plt.grid(True)
plt.ylim(-0.001, 1.1)
plt.ylabel('Global Pixels error %')
plt.legend(loc=1, fancybox=True, framealpha=0.1, fontsize='small')
# plot Mean Pixels error %
plt.subplot(3, 1, 2)
plt_range = range(len(errs))
plt.plot(plt_range, 1 - errs[:, 1], label='train')
plt.plot(plt_range, 1 - errs[:, 4], label='valid')
plt.plot(plt_range, 1 - errs[:, 7], label='test')
plt.grid(True)
plt.ylim(-0.001, 1.1)
plt.ylabel('Avg Class error %')
plt.legend(loc=1, fancybox=True, framealpha=0.1, fontsize='small')
# Plot Mean IoU error %
plt.subplot(3, 1, 3)
plt_range = range(len(errs))
plt.plot(plt_range, 1 - errs[:, 2], label='train')
plt.plot(plt_range, 1 - errs[:, 5], label='valid')
plt.plot(plt_range, 1 - errs[:, 8], label='test')
plt.grid(True)
plt.ylim(-0.001, 1.1)
plt.ylabel('Avg IoU error %')
plt.legend(loc=1, fancybox=True, framealpha=0.1, fontsize='small')
if huc is not None:
plt.subplot(2, 1, 2)
scale = float(len(errs)) / len(huc)
huc_range = [i * scale for i in range(len(huc))]
plt.plot(huc_range, huc)
plt.ylabel('Training cost')
plt.grid(True)
# plt.show()
plt.savefig(save_plot_path, format="pdf")
if print_history:
for i, (e, c, iou) in enumerate(zip(errs, conf_matrices, iou_indeces)):
(train_global_acc, train_mean_class_acc, train_mean_iou_index,
valid_global_acc, valid_mean_class_acc, valid_mean_iou_index,
test_global_acc, test_mean_class_acc, test_mean_iou_index) = e
(train_conf_matrix, valid_conf_matrix, test_conf_matrix) = c
# (train_iou_index, valid_iou_index, test_iou_index) = iou
print ""
print ""
print ""
print ""
headers_acc = ["Global Accuracies",
"Mean Class Accuracies",
"Mean Intersection Over Union"]
rows = list()
rows.append(['Train ',
round(train_global_acc, 6),
round(train_mean_class_acc, 6),
round(train_mean_iou_index, 6)])
rows.append(['Valid ',
round(valid_global_acc, 6),
round(valid_mean_class_acc, 6),
round(valid_mean_iou_index, 6)])
rows.append(['Test ', round(test_global_acc, 6),
round(test_mean_class_acc, 6),
round(test_mean_iou_index, 6)])
print(tabulate(rows, headers=headers_acc))
train_conf_matrix_norm = (train_conf_matrix /
train_conf_matrix.sum(axis=1))
valid_conf_matrix_norm = (valid_conf_matrix /
valid_conf_matrix.sum(axis=1))
test_conf_matrix_norm = (test_conf_matrix /
test_conf_matrix.sum(axis=1))
class_acc = list()
class_acc.append(numpy.concatenate([["Train"], numpy.round(
numpy.diagonal(train_conf_matrix_norm), 3)]))
class_acc.append(numpy.concatenate([["Valid"], numpy.round(
numpy.diagonal(valid_conf_matrix_norm), 3)]))
if len(test_conf_matrix) > 0:
class_acc.append(numpy.concatenate([["Test"], numpy.round(
numpy.diagonal(test_conf_matrix_norm), 3)]))
print(tabulate(class_acc, headers=headers))
if dataset != "nyu_depth40":
numpy.set_printoptions(precision=3)
print ""
print('Train Confusion matrix')
print(tabulate(train_conf_matrix_norm, headers=headers))
print ""
print('Valid Confusion matrix')
print(tabulate(valid_conf_matrix_norm, headers=headers))
if len(test_conf_matrix_norm) > 0:
print ""
print('Test Confusion matrix')
print(tabulate(test_conf_matrix_norm, headers=headers))
if i == -6:
break
return 1
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='Show the desired parameters of the network')
parser.add_argument(
'dataset',
default='horses',
help='The name of the esperiment.')
parser.add_argument(
'experiment',
default='',
nargs='?',
help='The of the esperiment.')
parser.add_argument(
'--plot',
'-p',
action='store_true',
help='Boolean. If set will plot the training curves')
parser.add_argument(
'--print-error-history',
'-peh',
action='store_true',
help='Boolean. If set will print the value of the different '
'metrics in every epoch')
parser.add_argument(
'--print_best_class_accuracy',
'-pca',
action='store_true',
help='Boolean. If set will print the best per-class accuracy')
parser.add_argument(
'--print-commit-hash',
'-ph',
action='store_true',
help='Boolean. If set will print the commit hash')
parser.add_argument(
'--model',
default='model_recseg',
help='The name of the model.')
parser.add_argument(
'--cycle',
'-c',
action='store_true',
help='Boolean. If set will cycle through all the available '
'saved models.')
parser.add_argument(
'--skip',
'-s',
nargs='*',
type=int,
default=[],
help='List of experiment to skip from the cycle')
args = parser.parse_args()
if not args.cycle:
print_pkl_params(args.dataset + '_models/' + args.model + '_' +
args.dataset + args.experiment + '.npz.pkl',
args.print_commit_hash, args.plot,
args.print_error_history,
args.print_best_class_accuracy)
else:
n = 0
ok = 1
while ok:
n += 1
if n in args.skip:
print ''
continue
ok = print_pkl_params(args.dataset + '_models/' + args.model +
'_' + args.dataset + str(n) + '.npz.pkl',
args.print_commit_hash, args.plot,
args.print_error_history,
args.print_best_class_accuracy)
if not ok:
ok = print_pkl_params('/Tmp/visin/' + args.dataset +
'_models/' + args.model + '_' +
args.dataset + str(n) + '.npz.pkl',
args.print_commit_hash, args.plot,
args.print_error_history,
args.print_best_class_accuracy)
print('Printed models from 1 to {}').format(n-1)
================================================
FILE: helper_dataset.py
================================================
import numpy as np
import os, sys
from numpy import sqrt, prod, ones, floor, repeat, pi, exp, zeros, sum
from numpy.random import RandomState
from theano.tensor.nnet import conv2d
from theano import shared, config, _asarray, function
import theano.tensor as T
floatX = config.floatX
from sklearn.feature_extraction.image import PatchExtractor
from sklearn.decomposition import PCA
from skimage import exposure
from skimage import io
from skimage import img_as_float, img_as_ubyte, img_as_uint, img_as_int
from skimage.color import label2rgb, rgb2hsv, hsv2rgb
from skimage.io import ImageCollection, imsave, imshow
from skimage.transform import resize
def compare_mask_image_filenames(filenames_images, filenames_mask,
replace_from='',
replace_to='',
msg="Filename images and mask mismatch"):
image = [i.split('/')[-1] for i in filenames_images]
mask = [i.split('/')[-1].replace(replace_from, replace_to) for i in
filenames_mask]
assert np.array_equal(image, mask), msg
def convert_RGB_mask_to_index(im, colors, ignore_missing_labels=False):
"""
:param im: mask in RGB format (classes are RGB colors)
:param colors: the color map should be in the following format
colors = OrderedDict([
("Sky", np.array([[128, 128, 128]], dtype=np.uint8)),
("Building", np.array([[128, 0, 0], # Building
[64, 192, 0], # Wall
[0, 128, 64] # Bridge
], dtype=np.uint8)
...
])
:param ignore_missing_labels: if True the function continue also if some
pixels fail the mappint
:return: the mask in index class format
"""
out = (np.ones(im.shape[:2]) * 255).astype(np.uint8)
for grey_val, (label, rgb) in enumerate(colors.items()):
for el in rgb:
match_pxls = np.where((im == np.asarray(el)).sum(-1) == 3)
out[match_pxls] = grey_val
if ignore_missing_labels: # retrieve the void label
if [0, 0, 0] in rgb:
void_label = grey_val
# debug
# outpath = '/Users/marcus/exp/datasets/camvid/grey_test/o.png'
# imsave(outpath, out)
######
if ignore_missing_labels:
match_missing = np.where(out == 255)
if match_missing[0].size > 0:
print "Ignoring missing labels"
out[match_missing] = void_label
assert (out != 255).all(), "rounding errors or missing classes in colors"
return out.astype(np.uint8)
def resize():
pass
def crop():
pass
def zero_pad(im, resize_size, inpath="", pad_value=0):
"""
:param im: the image you want to resize
:param resize_size: the new size of the image
:param inpath: [optional] to debug, the path of the image
:return: the zero-pad image in the new dimensions
"""
if im.ndim == 3:
h, w, _ = im.shape
elif im.ndim == 2:
h, w = im.shape
rw, rh = resize_size
pad_w = rw - w
pad_h = rh - h
pad_l = pad_r = pad_u = pad_d = 0
if pad_w > 0:
pad_l = int(pad_w / 2)
pad_r = pad_w - pad_l
if pad_h > 0:
pad_u = int(pad_h / 2)
pad_d = pad_h - pad_u
if im.ndim == 3:
im = np.pad(im, ((pad_u, pad_d), (pad_l, pad_r), (0, 0)),
mode='constant',
constant_values=pad_value)
elif im.ndim == 2:
im = np.pad(im, ((pad_u, pad_d), (pad_l, pad_r)),
mode='constant',
constant_values=pad_value)
assert (im.shape[1], im.shape[0]) == resize_size, \
"Resize size doesn't match: resize_size->{} resized->{}"\
" filename : {}".format(resize_size,
[im.shape[1], im.shape[0]],
inpath
)
return im
def rgb2illumination_invariant(img, alpha, hist_eq=False):
"""
this is an implementation of the illuminant-invariant color space published
by Maddern2014
http://www.robots.ox.ac.uk/~mobile/Papers/2014ICRA_maddern.pdf
:param img:
:param alpha: camera paramete
:return:
"""
ii_img = 0.5 + np.log(img[:, :, 1] + 1e-8) - \
alpha * np.log(img[:, :, 2] + 1e-8) - \
(1 - alpha) * np.log(img[:, :, 0] + 1e-8)
# ii_img = exposure.rescale_intensity(ii_img, out_range=(0, 1))
if hist_eq:
ii_img = exposure.equalize_hist(ii_img)
print np.max(ii_img)
print np.min(ii_img)
return ii_img
def save_image(outpath, img):
import errno
try:
os.makedirs(os.path.dirname(outpath))
except OSError as e:
if e.errno != errno.EEXIST:
raise e
pass
imsave(outpath, img)
def save_RGB_mask(outpath, mask):
return
def preprocess_dataset(train, valid, test,
input_to_float,
preprocess_type,
patch_size, max_patches):
if input_to_float and preprocess_type is None:
train_norm = train[0].astype(floatX) / 255.
train = (train_norm, train[1])
valid_norm = valid[0].astype(floatX) / 255.
valid = (valid_norm, valid[1])
test_norm = test[0].astype(floatX) / 255.
test = (test_norm, test[1])
if preprocess_type is None:
return train, valid, test
# whiten, LCN, GCN, Local Mean Subtract, or normalize
if len(train[0]) > 0:
train_pre = []
print ""
print "Preprocessing {} images of the train set with {} {} ".format(
len(train[0]), preprocess_type, patch_size),
print ""
i = 0
print "Progress: {0:.3g} %".format(i * 100 / len(train[0])),
for i, x in enumerate(train[0]):
img = np.expand_dims(x, axis=0)
x_pre = preprocess(img, preprocess_type,
patch_size,
max_patches)
train_pre.append(x_pre[0])
print "\rProgress: {0:.3g} %".format(i * 100 / len(train[0])),
sys.stdout.flush()
if input_to_float:
train_pre = np.array(train_pre).astype(floatX) / 255.
train = (np.array(train_pre), np.array(train[1]))
if len(valid[0]) > 0:
valid_pre = []
print ""
print "Preprocessing {} images of the valid set with {} {} ".format(
len(valid[0]), preprocess_type, patch_size),
print ""
i = 0
print "Progress: {0:.3g} %".format(i * 100 / len(valid[0])),
for i, x in enumerate(valid[0]):
img = np.expand_dims(x, axis=0)
x_pre = preprocess(img, preprocess_type,
patch_size,
max_patches)
valid_pre.append(x_pre[0])
print "\rProgress: {0:.3g} %".format(i * 100 / len(valid[0])),
sys.stdout.flush()
if input_to_float:
valid_pre = np.array(valid_pre).astype(floatX) / 255.
valid = (np.array(valid_pre), np.array(valid[1]))
if len(test[0]) > 0:
test_pre = []
print ""
print "Preprocessing {} images of the test set with {} {} ".format(
len(test[0]), preprocess_type, patch_size),
print ""
i = 0
print "Progress: {0:.3g} %".format(i * 100 / len(test[0])),
for i, x in enumerate(test[0]):
img = np.expand_dims(x, axis=0)
x_pre = preprocess(img, preprocess_type,
patch_size,
max_patches)
test_pre.append(x_pre[0])
print "\rProgress: {0:.3g} %".format(i * 100 / len(test[0])),
sys.stdout.flush()
if input_to_float:
test_pre = np.array(test_pre).astype(floatX) / 255.
test = (np.array(test_pre), np.array(test[1]))
return train, valid, test
def preprocess(x, mode=None,
patch_size=9,
max_patches=int(1e5)):
"""
:param x:
:param mode:
:param rng:
:param patch_size:
:param max_patches:
:return:
"""
if mode == 'conv-zca':
x = convolutional_zca(x,
patch_size=patch_size,
max_patches=max_patches)
elif mode == 'sub-lcn':
for d in range(x.shape[-1]):
x[:, :, :, d] = lecun_lcn(x[:, :, :, d],
kernel_size=patch_size)
elif mode == 'subdiv-lcn':
for d in range(x.shape[-1]):
x[:, :, :, d] = lecun_lcn(x[:, :, :, d],
kernel_size=patch_size,
use_divisor=True)
elif mode == 'gcn':
for d in range(x.shape[-1]):
x[:, :, :, d] = global_contrast_normalization(x[:, :, :, d])
elif mode == 'local_mean_sub':
for d in range(x.shape[-1]):
x[:, :, :, d] = local_mean_subtraction(x[:, :, :, d],
kernel_size=patch_size)
# x = x.astype(floatX)
return x
def lecun_lcn(input, kernel_size=9, threshold=1e-4, use_divisor=False):
"""
Yann LeCun's local contrast normalization
Orginal code in Theano by: Guillaume Desjardins
:param input:
:param kernel_size:
:param threshold:
:param use_divisor:
:return:
"""
input_shape = (input.shape[0], 1, input.shape[1], input.shape[2])
input = input.reshape(input_shape).astype(floatX)
X = T.tensor4(dtype=floatX)
filter_shape = (1, 1, kernel_size, kernel_size)
filters = gaussian_filter(kernel_size).reshape(filter_shape)
filters = shared(_asarray(filters, dtype=floatX), borrow=True)
convout = conv2d(input=X,
filters=filters,
input_shape=input.shape,
filter_shape=filter_shape,
border_mode='half')
new_X = X - convout
if use_divisor:
# Scale down norm of kernel_size x kernel_size patch
sum_sqr_XX = conv2d(input=T.sqr(T.abs_(new_X)),
filters=filters,
input_shape=input.shape,
filter_shape=filter_shape,
border_mode='half')
denom = T.sqrt(sum_sqr_XX)
per_img_mean = denom.mean(axis=[2, 3])
divisor = T.largest(per_img_mean.dimshuffle(0, 1, 'x', 'x'), denom)
divisor = T.maximum(divisor, threshold)
new_X = new_X / divisor
new_X = new_X.dimshuffle(0, 2, 3, 1)
new_X = new_X.flatten(ndim=3)
f = function([X], new_X)
return f(input)
def local_mean_subtraction(input, kernel_size=5):
input_shape = (input.shape[0], 1, input.shape[1], input.shape[2])
input = input.reshape(input_shape).astype(floatX)
X = T.tensor4(dtype=floatX)
filter_shape = (1, 1, kernel_size, kernel_size)
filters = mean_filter(kernel_size).reshape(filter_shape)
filters = shared(_asarray(filters, dtype=floatX), borrow=True)
mean = conv2d(input=X,
filters=filters,
input_shape=input.shape,
filter_shape=filter_shape,
border_mode='half')
new_X = X - mean
f = function([X], new_X)
return f(input)
def global_contrast_normalization(input, scale=1., subtract_mean=True,
use_std=False, sqrt_bias=0., min_divisor=1e-8):
input_shape = (input.shape[0], 1, input.shape[1], input.shape[2])
input = input.reshape(input_shape).astype(floatX)
X = T.tensor4(dtype=floatX)
ndim = X.ndim
if not ndim in [3, 4]:
raise NotImplementedError("X.dim>4 or X.ndim<3")
scale = float(scale)
mean = X.mean(axis=ndim-1)
new_X = X.copy()
if subtract_mean:
if ndim == 3:
new_X = X - mean[:, :, None]
else:
new_X = X - mean[:, :, :, None]
if use_std:
normalizers = T.sqrt(sqrt_bias + X.var(axis=ndim-1)) / scale
else:
normalizers = T.sqrt(sqrt_bias + (new_X ** 2).sum(axis=ndim-1)) / scale
# Don't normalize by anything too small.
T.set_subtensor(normalizers[(normalizers < min_divisor).nonzero()], 1.)
if ndim == 3:
new_X /= normalizers[:, :, None]
else:
new_X /= normalizers[:, :, :, None]
f = function([X], new_X)
return f(input)
def gaussian_filter(kernel_shape):
x = zeros((kernel_shape, kernel_shape), dtype='float32')
def gauss(x, y, sigma=2.0):
Z = 2 * pi * sigma**2
return 1./Z * exp(-(x**2 + y**2) / (2. * sigma**2))
mid = floor(kernel_shape/ 2.)
for i in xrange(0,kernel_shape):
for j in xrange(0,kernel_shape):
x[i, j] = gauss(i-mid, j-mid)
return x / sum(x)
def mean_filter(kernel_size):
s = kernel_size**2
x = repeat(1. / s, s).reshape((kernel_size, kernel_size))
return x
def convolutional_zca(input, patch_size=(9, 9), max_patches=int(1e5)):
"""
This is an implementation of the convolutional ZCA whitening presented by
David Eigen in his phd thesis
http://www.cs.nyu.edu/~deigen/deigen-thesis.pdf
"Predicting Images using Convolutional Networks:
Visual Scene Understanding with Pixel Maps"
From paragraph 8.4:
A simple adaptation of ZCA to convolutional application is to find the
ZCA whitening transformation for a sample of local image patches across
the dataset, and then apply this transform to every patch in a larger image.
We then use the center pixel of each ZCA patch to create the conv-ZCA
output image. The operations of applying local ZCA and selecting the center
pixel can be combined into a single convolution kernel,
resulting in the following algorithm
(explained using RGB inputs and 9x9 kernel):
1. Sample 10M random 9x9 image patches (each with 3 colors)
2. Perform PCA on these to get eigenvectors V and eigenvalues D.
3. Optionally remove small eigenvalues, so V has shape [npca x 3 x 9 x 9].
4. Construct the whitening kernel k:
for each pair of colors (ci,cj),
set k[j,i, :, :] = V[:, j, x0, y0]^T * D^{-1/2} * V[:, i, :, :]
where (x0, y0) is the center pixel location (e.g. (5,5) for a 9x9 kernel)
:param input: 4D tensor of shape [batch_size, rows, col, channels]
:param patch_size: size of the patches extracted from the dataset
:param max_patches: max number of patches extracted from the dataset
:return: conv-zca whitened dataset
"""
# I don't know if it's correct or not.. but it seems to work
mean = np.mean(input, axis=(0, 1, 2))
input -= mean # center the data
n_imgs, h, w, n_channels = input.shape
patch_size = (patch_size, patch_size)
patches = PatchExtractor(patch_size=patch_size,
max_patches=max_patches).transform(input)
pca = PCA()
pca.fit(patches.reshape(patches.shape[0], -1))
# Transpose the components into theano convolution filter type
dim = (-1,) + patch_size + (n_channels,)
V = shared(pca.components_.reshape(dim).
transpose(0, 3, 1, 2).astype(input.dtype))
D = T.nlinalg.diag(1. / np.sqrt(pca.explained_variance_))
x_0 = int(np.floor(patch_size[0] / 2))
y_0 = int(np.floor(patch_size[1] / 2))
filter_shape = [n_channels, n_channels, patch_size[0], patch_size[1]]
image_shape = [n_imgs, n_channels, h, w]
kernel = T.zeros(filter_shape)
VT = V.dimshuffle(2, 3, 1, 0)
# V : 243 x 3 x 9 x 9
# VT : 9 x 9 x 3 x 243
# build the kernel
for i in range(n_channels):
for j in range(n_channels):
a = T.dot(VT[x_0, y_0, j, :], D).reshape([1, -1])
b = V[:, i, :, :].reshape([-1, patch_size[0] * patch_size[1]])
c = T.dot(a, b).reshape([patch_size[0], patch_size[1]])
kernel = T.set_subtensor(kernel[j, i, :, :], c)
kernel = kernel.astype(floatX)
input = input.astype(floatX)
input_images = T.tensor4(dtype=floatX)
conv_whitening = conv2d(input_images.dimshuffle((0, 3, 1, 2)),
kernel,
input_shape=image_shape,
filter_shape=filter_shape,
border_mode='full')
s_crop = [(patch_size[0] - 1) // 2,
(patch_size[1] - 1) // 2]
# e_crop = [s_crop[0] if (s_crop[0] % 2) != 0 else s_crop[0] + 1,
# s_crop[1] if (s_crop[1] % 2) != 0 else s_crop[1] + 1]
conv_whitening = conv_whitening[:, :, s_crop[0]:-s_crop[0], s_crop[
1]:-s_crop[1]]
conv_whitening = conv_whitening.dimshuffle(0, 2, 3, 1)
f_convZCA = function([input_images], conv_whitening)
return f_convZCA(input)
================================================
FILE: layers.py
================================================
from collections import Iterable
import numpy as np
import lasagne
from lasagne.layers import get_output, get_output_shape
from lasagne.layers.conv import TransposedConv2DLayer
import theano.tensor as T
from padded import DynamicPaddingLayer, PaddedConv2DLayer as ConvLayer
from utils import ceildiv, to_int
class ReSegLayer(lasagne.layers.Layer):
def __init__(self,
l_in,
n_layers,
pheight,
pwidth,
dim_proj,
nclasses,
stack_sublayers,
# outsampling
out_upsampling_type,
out_nfilters,
out_filters_size,
out_filters_stride,
out_W_init=lasagne.init.GlorotUniform(),
out_b_init=lasagne.init.Constant(0.),
out_nonlinearity=lasagne.nonlinearities.identity,
hypotetical_fm_size=np.array((100.0, 100.0)),
# input ConvLayers
in_nfilters=None,
in_filters_size=((3, 3), (3, 3)),
in_filters_stride=((1, 1), (1, 1)),
in_W_init=lasagne.init.GlorotUniform(),
in_b_init=lasagne.init.Constant(0.),
in_nonlinearity=lasagne.nonlinearities.rectify,
in_vgg_layer='conv3_3',
# common recurrent layer params
RecurrentNet=lasagne.layers.GRULayer,
nonlinearity=lasagne.nonlinearities.rectify,
hid_init=lasagne.init.Constant(0.),
grad_clipping=0,
precompute_input=True,
mask_input=None,
# 1x1 Conv layer for dimensional reduction
conv_dim_red=False,
conv_dim_red_nonlinearity=lasagne.nonlinearities.identity,
# GRU specific params
gru_resetgate=lasagne.layers.Gate(W_cell=None),
gru_updategate=lasagne.layers.Gate(W_cell=None),
gru_hidden_update=lasagne.layers.Gate(
W_cell=None,
nonlinearity=lasagne.nonlinearities.tanh),
gru_hid_init=lasagne.init.Constant(0.),
# LSTM specific params
lstm_ingate=lasagne.layers.Gate(),
lstm_forgetgate=lasagne.layers.Gate(),
lstm_cell=lasagne.layers.Gate(
W_cell=None,
nonlinearity=lasagne.nonlinearities.tanh),
lstm_outgate=lasagne.layers.Gate(),
# RNN specific params
rnn_W_in_to_hid=lasagne.init.Uniform(),
rnn_W_hid_to_hid=lasagne.init.Uniform(),
rnn_b=lasagne.init.Constant(0.),
# Special layers
batch_norm=False,
name=''):
"""A ReSeg layer
The ReSeg layer is composed by multiple ReNet layers and an
upsampling layer
Parameters
----------
l_in : lasagne.layers.Layer
The input layer, in bc01 format
n_layers : int
The number of layers
pheight : tuple
The height of the patches, for each layer
pwidth : tuple
The width of the patches, for each layer
dim_proj : tuple
The number of hidden units of each RNN, for each layer
nclasses : int
The number of classes of the data
stack_sublayers : bool
If True the bidirectional RNNs in the ReNet layers will be
stacked one over the other. See ReNet for more details.
out_upsampling_type : string
The kind of upsampling to be used
out_nfilters : int
The number of hidden units of the upsampling layer
out_filters_size : tuple
The size of the upsampling filters, if any
out_filters_stride : tuple
The stride of the upsampling filters, if any
out_W_init : Theano shared variable, numpy array or callable
Initializer for W
out_b_init : Theano shared variable, numpy array or callable
Initializer for b
out_nonlinearity : Theano shared variable, numpy array or callable
The nonlinearity to be applied after the upsampling
hypotetical_fm_size : float
The hypotetical size of the feature map that would be input
of the layer if the input image of the whole network was of
size (100, 100)
RecurrentNet : lasagne.layers.Layer
A recurrent layer class
nonlinearity : callable or None
The nonlinearity that is applied to the output. If
None is provided, no nonlinearity will be applied.
hid_init : callable, np.ndarray, theano.shared or
lasagne.layers.Layer
Initializer for initial hidden state
grad_clipping : float
If nonzero, the gradient messages are clipped to the given value
during the backward pass.
precompute_input : bool
If True, precompute input_to_hid before iterating through the
sequence. This can result in a speedup at the expense of an
increase in memory usage.
mask_input : lasagne.layers.Layer
Layer which allows for a sequence mask to be input, for when
sequences are of variable length. Default None, which means no mask
will be supplied (i.e. all sequences are of the same length).
gru_resetgate : lasagne.layers.Gate
Parameters for the reset gate, if RecurrentNet is GRU
gru_updategate : lasagne.layers.Gate
Parameters for the update gate, if RecurrentNet is GRU
gru_hidden_update : lasagne.layers.Gate
Parameters for the hidden update, if RecurrentNet is GRU
gru_hid_init : callable, np.ndarray, theano.shared or
lasagne.layers.Layer
Initializer for initial hidden state, if RecurrentNet is GRU
lstm_ingate : lasagne.layers.Gate
Parameters for the input gate, if RecurrentNet is LSTM
lstm_forgetgate : lasagne.layers.Gate
Parameters for the forget gate, if RecurrentNet is LSTM
lstm_cell : lasagne.layers.Gate
Parameters for the cell computation, if RecurrentNet is LSTM
lstm_outgate : lasagne.layers.Gate
Parameters for the output gate, if RecurrentNet is LSTM
rnn_W_in_to_hid : Theano shared variable, numpy array or callable
Initializer for input-to-hidden weight matrix, if
RecurrentNet is RecurrentLayer
rnn_W_hid_to_hid : Theano shared variable, numpy array or callable
Initializer for hidden-to-hidden weight matrix, if
RecurrentNet is RecurrentLayer
rnn_b : Theano shared variable, numpy array, callable or None
Initializer for bias vector, if RecurrentNet is
RecurrentLaye. If None is provided there will be no bias
batch_norm: this add a batch normalization layer at the end of the
network right after each Gradient Upsampling layers
name : string
The name of the layer, optional
"""
super(ReSegLayer, self).__init__(l_in, name)
self.l_in = l_in
self.n_layers = n_layers
self.pheight = pheight
self.pwidth = pwidth
self.dim_proj = dim_proj
self.nclasses = nclasses
self.stack_sublayers = stack_sublayers
# upsampling
self.out_upsampling_type = out_upsampling_type
self.out_nfilters = out_nfilters
self.out_filters_size = out_filters_size
self.out_filters_stride = out_filters_stride
self.out_W_init = out_W_init
self.out_b_init = out_b_init
self.out_nonlinearity = out_nonlinearity
self.hypotetical_fm_size = hypotetical_fm_size
# input ConvLayers
self.in_nfilters = in_nfilters
self.in_filters_size = in_filters_size
self.in_filters_stride = in_filters_stride
self.in_W_init = in_W_init
self.in_b_init = in_b_init
self.in_nonlinearity = in_nonlinearity
self.in_vgg_layer = in_vgg_layer
# common recurrent layer params
self.RecurrentNet = RecurrentNet
self.nonlinearity = nonlinearity
self.hid_init = hid_init
self.grad_clipping = grad_clipping
self.precompute_input = precompute_input
self.mask_input = mask_input
# GRU specific params
self.gru_resetgate = gru_resetgate
self.gru_updategate = gru_updategate
self.gru_hidden_update = gru_hidden_update
self.gru_hid_init = gru_hid_init
# LSTM specific params
self.lstm_ingate = lstm_ingate
self.lstm_forgetgate = lstm_forgetgate
self.lstm_cell = lstm_cell
self.lstm_outgate = lstm_outgate
# RNN specific params
self.rnn_W_in_to_hid = rnn_W_in_to_hid
self.rnn_W_hid_to_hid = rnn_W_hid_to_hid
self.name = name
self.sublayers = []
expand_height = expand_width = 1
# Input ConvLayers
l_conv = l_in
if isinstance(in_nfilters, Iterable) and not isinstance(in_nfilters,
str):
for i, (nf, f_size, stride) in enumerate(
zip(in_nfilters, in_filters_size, in_filters_stride)):
l_conv = ConvLayer(
l_conv,
num_filters=nf,
filter_size=f_size,
stride=stride,
W=in_W_init,
b=in_b_init,
pad='valid',
name=self.name + '_input_conv_layer' + str(i)
)
self.sublayers.append(l_conv)
self.hypotetical_fm_size = (
(self.hypotetical_fm_size - 1) * stride + f_size)
# TODO This is right only if stride == filter...
expand_height *= f_size[0]
expand_width *= f_size[1]
# Print shape
out_shape = get_output_shape(l_conv)
print('ConvNet: After in-convnet: {}'.format(out_shape))
# Pretrained vgg16
elif type(in_nfilters) == str:
from vgg16 import Vgg16Layer
l_conv = Vgg16Layer(l_in, self.in_nfilters, False, False)
hypotetical_fm_size /= 8
expand_height = expand_width = 8
self.sublayers.append(l_conv)
# Print shape
out_shape = get_output_shape(l_conv)
print('Vgg: After vgg: {}'.format(out_shape))
# ReNet layers
l_renet = l_conv
for lidx in xrange(n_layers):
l_renet = ReNetLayer(l_renet,
patch_size=(pwidth[lidx], pheight[lidx]),
n_hidden=dim_proj[lidx],
stack_sublayers=stack_sublayers[lidx],
RecurrentNet=RecurrentNet,
nonlinearity=nonlinearity,
hid_init=hid_init,
grad_clipping=grad_clipping,
precompute_input=precompute_input,
mask_input=mask_input,
# GRU specific params
gru_resetgate=gru_resetgate,
gru_updategate=gru_updategate,
gru_hidden_update=gru_hidden_update,
gru_hid_init=gru_hid_init,
# LSTM specific params
lstm_ingate=lstm_ingate,
lstm_forgetgate=lstm_forgetgate,
lstm_cell=lstm_cell,
lstm_outgate=lstm_outgate,
# RNN specific params
rnn_W_in_to_hid=rnn_W_in_to_hid,
rnn_W_hid_to_hid=rnn_W_hid_to_hid,
rnn_b=rnn_b,
batch_norm=batch_norm,
name=self.name + '_renet' + str(lidx))
self.sublayers.append(l_renet)
self.hypotetical_fm_size /= (pwidth[lidx], pheight[lidx])
# Print shape
out_shape = get_output_shape(l_renet)
if stack_sublayers:
msg = 'ReNet: After 2 rnns {}x{}@{} and 2 rnns 1x1@{}: {}'
print(msg.format(pheight[lidx], pwidth[lidx], dim_proj[lidx],
dim_proj[lidx], out_shape))
else:
print('ReNet: After 4 rnns {}x{}@{}: {}'.format(
pheight[lidx], pwidth[lidx], dim_proj[lidx], out_shape))
# 1x1 conv layer : dimensionality reduction layer
if conv_dim_red:
l_renet = lasagne.layers.Conv2DLayer(
l_renet,
num_filters=dim_proj[lidx],
filter_size=(1, 1),
W=lasagne.init.GlorotUniform(),
b=lasagne.init.Constant(0.),
pad='valid',
nonlinearity=conv_dim_red_nonlinearity,
name=self.name + '_1x1_conv_layer' + str(lidx)
)
# Print shape
out_shape = get_output_shape(l_renet)
print('Dim reduction: After 1x1 convnet: {}'.format(out_shape))
# Upsampling
if out_upsampling_type == 'autograd':
raise NotImplementedError(
'This will not work as the dynamic cropping will crop '
'part of the image.')
nlayers = len(out_nfilters)
assert nlayers > 1
# Compute the upsampling ratio and the corresponding params
h2 = np.array((100., 100.))
up_ratio = (h2 / self.hypotetical_fm_size) ** (1. / nlayers)
h1 = h2 / up_ratio
h0 = h1 / up_ratio
stride = to_int(ceildiv(h2 - h1, h1 - h0))
filter_size = to_int(ceildiv((h1 * (h1 - 1) + h2 - h2 * h0),
(h1 - h0)))
target_shape = get_output(l_renet).shape[2:]
l_upsampling = l_renet
for l in range(nlayers):
target_shape = target_shape * up_ratio
l_upsampling = TransposedConv2DLayer(
l_upsampling,
num_filters=out_nfilters[l],
filter_size=filter_size,
stride=stride,
W=out_W_init,
b=out_b_init,
nonlinearity=out_nonlinearity)
self.sublayers.append(l_upsampling)
up_shape = get_output(l_upsampling).shape[2:]
# Print shape
out_shape = get_output_shape(l_upsampling)
print('Transposed autograd: {}x{} (str {}x{}) @ {}:{}'.format(
filter_size[0], filter_size[1], stride[0], stride[1],
out_nfilters[l], out_shape))
# CROP
# pad in TransposeConv2DLayer cannot be a tensor --> we cannot
# crop unless we know in advance by how much!
crop = T.max(T.stack([up_shape - target_shape, T.zeros(2)]),
axis=0)
crop = crop.astype('uint8') # round down
l_upsampling = CropLayer(
l_upsampling,
crop,
data_format='bc01')
self.sublayers.append(l_upsampling)
# Print shape
print('Dynamic cropping')
elif out_upsampling_type == 'grad':
l_upsampling = l_renet
for i, (nf, f_size, stride) in enumerate(zip(
out_nfilters, out_filters_size, out_filters_stride)):
l_upsampling = TransposedConv2DLayer(
l_upsampling,
num_filters=nf,
filter_size=f_size,
stride=stride,
crop=0,
W=out_W_init,
b=out_b_init,
nonlinearity=out_nonlinearity)
self.sublayers.append(l_upsampling)
if batch_norm:
l_upsampling = lasagne.layers.batch_norm(
l_upsampling,
axes='auto')
self.sublayers.append(l_upsampling)
print "Batch normalization after Grad layer "
# Print shape
out_shape = get_output_shape(l_upsampling)
print('Transposed conv: {}x{} (str {}x{}) @ {}:{}'.format(
f_size[0], f_size[1], stride[0], stride[1], nf, out_shape))
elif out_upsampling_type == 'linear':
# Go to b01c
l_upsampling = lasagne.layers.DimshuffleLayer(
l_renet,
(0, 2, 3, 1),
name=self.name + '_grad_undimshuffle')
self.sublayers.append(l_upsampling)
expand_height *= np.prod(pheight)
expand_width *= np.prod(pwidth)
l_upsampling = LinearUpsamplingLayer(l_upsampling,
expand_height,
expand_width,
nclasses,
batch_norm=batch_norm,
name="linear_upsample_layer")
self.sublayers.append(l_upsampling)
print('Linear upsampling')
if batch_norm:
l_upsampling = lasagne.layers.batch_norm(
l_upsampling,
axes=(0, 1, 2))
self.sublayers.append(l_upsampling)
print "Batch normalization after Linear upsampling layer "
# Go back to bc01
l_upsampling = lasagne.layers.DimshuffleLayer(
l_upsampling,
(0, 3, 1, 2),
name=self.name + '_grad_undimshuffle')
self.sublayers.append(l_upsampling)
self.l_out = l_upsampling
# HACK LASAGNE
# This will set `self.input_layer`, which is needed by Lasagne to find
# the layers with the get_all_layers() helper function in the
# case of a layer with sublayers
if isinstance(self.l_out, tuple):
self.input_layer = None
else:
self.input_layer = self.l_out
def get_output_shape_for(self, input_shape):
for layer in self.sublayers:
output_shape = layer.get_output_shape_for(input_shape)
input_shape = output_shape
return output_shape
# return self.l_out.get_output_shape_for(input_shape)
# return list(input_shape[0:3]) + [self.nclasses]
def get_output_for(self, input_var, **kwargs):
# HACK LASAGNE
# This is needed, jointly with the previous hack, to ensure that
# this layer behaves as its last sublayer (namely,
# self.input_layer)
return input_var
class ReNetLayer(lasagne.layers.Layer):
def __init__(self,
l_in,
patch_size=(2, 2),
n_hidden=50,
stack_sublayers=False,
RecurrentNet=lasagne.layers.GRULayer,
nonlinearity=lasagne.nonlinearities.rectify,
hid_init=lasagne.init.Constant(0.),
grad_clipping=0,
precompute_input=True,
mask_input=None,
# GRU specific params
gru_resetgate=lasagne.layers.Gate(W_cell=None),
gru_updategate=lasagne.layers.Gate(W_cell=None),
gru_hidden_update=lasagne.layers.Gate(
W_cell=None,
nonlinearity=lasagne.nonlinearities.tanh),
gru_hid_init=lasagne.init.Constant(0.),
# LSTM specific params
lstm_ingate=lasagne.layers.Gate(),
lstm_forgetgate=lasagne.layers.Gate(),
lstm_cell=lasagne.layers.Gate(
W_cell=None,
nonlinearity=lasagne.nonlinearities.tanh),
lstm_outgate=lasagne.layers.Gate(),
# RNN specific params
rnn_W_in_to_hid=lasagne.init.Uniform(),
rnn_W_hid_to_hid=lasagne.init.Uniform(),
rnn_b=lasagne.init.Constant(0.),
batch_norm=False,
name='', **kwargs):
"""A ReNet layer
Each ReNet layer is composed by 4 RNNs (or 2 bidirectional RNNs):
* First SubLayer:
2 RNNs scan the image vertically (up and down)
* Second Sublayer:
2 RNNs scan the image horizontally (left and right)
The sublayers can be stacked one over the other or can scan the
image in parallel
Parameters
----------
l_in : lasagne.layers.Layer
The input layer, in format batches, channels, rows, cols
patch_size : tuple
The size of the patch expressed as (pheight, pwidth).
Optional
n_hidden : int
The number of hidden units of each RNN. Optional
stack_sublayers : bool
If True, the sublayers (i.e. the bidirectional RNNs) will be
stacked one over the other, meaning that the second
bidirectional RNN will read the feature map coming from the
first bidirectional RNN. If False, all the RNNs will read
the input. Optional
RecurrentNet : lasagne.layers.Layer
A recurrent layer class
nonlinearity : callable or None
The nonlinearity that is applied to the output. If
None is provided, no nonlinearity will be applied.
hid_init : callable, np.ndarray, theano.shared or
lasagne.layers.Layer
Initializer for initial hidden state
grad_clipping : float
If nonzero, the gradient messages are clipped to the given value
during the backward pass.
precompute_input : bool
If True, precompute input_to_hid before iterating through the
sequence. This can result in a speedup at the expense of an
increase in memory usage.
mask_input : lasagne.layers.Layer
Layer which allows for a sequence mask to be input, for when
sequences are of variable length. Default None, which means no mask
will be supplied (i.e. all sequences are of the same length).
gru_resetgate : lasagne.layers.Gate
Parameters for the reset gate, if RecurrentNet is GRU
gru_updategate : lasagne.layers.Gate
Parameters for the update gate, if RecurrentNet is GRU
gru_hidden_update : lasagne.layers.Gate
Parameters for the hidden update, if RecurrentNet is GRU
gru_hid_init : callable, np.ndarray, theano.shared or
lasagne.layers.Layer
Initializer for initial hidden state, if RecurrentNet is GRU
lstm_ingate : lasagne.layers.Gate
Parameters for the input gate, if RecurrentNet is LSTM
lstm_forgetgate : lasagne.layers.Gate
Parameters for the forget gate, if RecurrentNet is LSTM
lstm_cell : lasagne.layers.Gate
Parameters for the cell computation, if RecurrentNet is LSTM
lstm_outgate : lasagne.layers.Gate
Parameters for the output gate, if RecurrentNet is LSTM
rnn_W_in_to_hid : Theano shared variable, numpy array or callable
Initializer for input-to-hidden weight matrix, if
RecurrentNet is RecurrentLayer
rnn_W_hid_to_hid : Theano shared variable, numpy array or callable
Initializer for hidden-to-hidden weight matrix, if
RecurrentNet is RecurrentLayer
rnn_b : Theano shared variable, numpy array, callable or None
Initializer for bias vector, if RecurrentNet is
RecurrentLaye. If None is provided there will be no bias
name : string
The name of the layer, optional
"""
super(ReNetLayer, self).__init__(l_in, name)
self.l_in = l_in
self.patch_size = patch_size
self.n_hidden = n_hidden
self.stack_sublayers = stack_sublayers
self.name = name
self.stride = self.patch_size # for now, it's not parametrized
# Dynamically add padding if the input is not a multiple of the
# patch size (expected input format: bs, ch, rows, cols)
l_in = DynamicPaddingLayer(l_in, patch_size, self.stride,
name=self.name + '_padding')
# get_output(l_in).shape will result in an error in the
# recurrent layers
batch_size = -1
cchannels, cheight, cwidth = get_output_shape(l_in)[1:]
pheight, pwidth = patch_size
psize = pheight * pwidth * cchannels
# Number of patches in each direction
npatchesH = cheight / pheight
npatchesW = cwidth / pwidth
# Split in patches: bs, cc, #H, ph, #W, pw
l_in = lasagne.layers.ReshapeLayer(
l_in,
(batch_size, cchannels, npatchesH, pheight, npatchesW, pwidth),
name=self.name + "_pre_reshape0")
# bs, #H, #W, ph, pw, cc
l_in = lasagne.layers.DimshuffleLayer(
l_in,
(0, 2, 4, 3, 5, 1),
name=self.name + "_pre_dimshuffle0")
# FIRST SUBLAYER
# The RNN Layer needs a 3D tensor input: bs*#H, #W, psize
# bs*#H, #W, ph * pw * cc
l_sub0 = lasagne.layers.ReshapeLayer(
l_in,
(-1, npatchesW, psize),
name=self.name + "_sub0_reshape0")
# Left/right scan: bs*#H, #W, 2*hid
l_sub0 = BidirectionalRNNLayer(
l_sub0,
n_hidden,
RecurrentNet=RecurrentNet,
nonlinearity=nonlinearity,
hid_init=hid_init,
grad_clipping=grad_clipping,
precompute_input=precompute_input,
mask_input=mask_input,
# GRU specific params
gru_resetgate=gru_resetgate,
gru_updategate=gru_updategate,
gru_hidden_update=gru_hidden_update,
gru_hid_init=gru_hid_init,
batch_norm=batch_norm,
# LSTM specific params
lstm_ingate=lstm_ingate,
lstm_forgetgate=lstm_forgetgate,
lstm_cell=lstm_cell,
lstm_outgate=lstm_outgate,
# RNN specific params
rnn_W_in_to_hid=rnn_W_in_to_hid,
rnn_W_hid_to_hid=rnn_W_hid_to_hid,
rnn_b=rnn_b,
name=self.name + "_sub0_renetsub")
# Revert reshape: bs, #H, #W, 2*hid
l_sub0 = lasagne.layers.ReshapeLayer(
l_sub0,
(batch_size, npatchesH, npatchesW, 2 * n_hidden),
name=self.name + "_sub0_unreshape")
# # Invert rows and columns: #H, bs, #W, 2*hid
# l_sub0 = lasagne.layers.DimshuffleLayer(
# l_sub0,
# (2, 1, 0, 3),
# name=self.name + "_sub0_undimshuffle")
# If stack_sublayers is True, the second sublayer takes as an input the
# first sublayer's output, otherwise the input of the ReNetLayer (e.g
# the image)
if stack_sublayers:
# bs, #H, #W, 2*hid
input_sublayer1 = l_sub0
psize = 2 * n_hidden
else:
# # #H, bs, #W, ph, pw, cc
# input_sublayer1 = lasagne.layers.DimshuffleLayer(
# l_in,
# (2, 1, 0, 3, 4, 5),
# name=self.name + "_presub1_in_dimshuffle")
# bs, #H, #W, ph*pw*cc
input_sublayer1 = lasagne.layers.ReshapeLayer(
l_in,
(batch_size, npatchesH, npatchesW, psize),
name=self.name + "_presub1_in_dimshuffle")
# SECOND SUBLAYER
# Invert rows and columns: bs, #W, #H, psize
l_sub1 = lasagne.layers.DimshuffleLayer(
input_sublayer1,
(0, 2, 1, 3),
name=self.name + "_presub1_dimshuffle")
# The RNN Layer needs a 3D tensor input: bs*#W, #H, psize
l_sub1 = lasagne.layers.ReshapeLayer(
l_sub1,
(-1, npatchesH, psize),
name=self.name + "_sub1_reshape")
# Down/up scan: bs*#W, #H, 2*hid
l_sub1 = BidirectionalRNNLayer(
l_sub1,
n_hidden,
RecurrentNet=RecurrentNet,
nonlinearity=nonlinearity,
hid_init=hid_init,
grad_clipping=grad_clipping,
precompute_input=precompute_input,
mask_input=mask_input,
# GRU specific params
gru_resetgate=gru_resetgate,
gru_updategate=gru_updategate,
gru_hidden_update=gru_hidden_update,
gru_hid_init=gru_hid_init,
# LSTM specific params
lstm_ingate=lstm_ingate,
lstm_forgetgate=lstm_forgetgate,
lstm_cell=lstm_cell,
lstm_outgate=lstm_outgate,
# RNN specific params
rnn_W_in_to_hid=rnn_W_in_to_hid,
rnn_W_hid_to_hid=rnn_W_hid_to_hid,
rnn_b=rnn_b,
name=self.name + "_sub1_renetsub")
psize = 2 * n_hidden
# Revert the reshape: bs, #W, #H, 2*hid
l_sub1 = lasagne.layers.ReshapeLayer(
l_sub1,
(batch_size, npatchesW, npatchesH, psize),
name=self.name + "_sub1_unreshape")
# Invert rows and columns: bs, #H, #W, psize
l_sub1 = lasagne.layers.DimshuffleLayer(
l_sub1,
(0, 2, 1, 3),
name=self.name + "_sub1_undimshuffle")
# Concat all 4 layers if needed: bs, #H, #W, {2,4}*hid
if not stack_sublayers:
l_sub1 = lasagne.layers.ConcatLayer([l_sub0, l_sub1], axis=3)
# Get back to bc01: bs, psize, #H, #W
self.out_layer = lasagne.layers.DimshuffleLayer(
l_sub1,
(0, 3, 1, 2),
name=self.name + "_out_undimshuffle")
# HACK LASAGNE
# This will set `self.input_layer`, which is needed by Lasagne to find
# the layers with the get_all_layers() helper function in the
# case of a layer with sublayers
if isinstance(self.out_layer, tuple):
self.input_layer = None
else:
self.input_layer = self.out_layer
def get_output_shape_for(self, input_shape):
pheight, pwidth = self.patch_size
npatchesH = ceildiv(input_shape[2], pheight)
npatchesW = ceildiv(input_shape[3], pwidth)
if self.stack_sublayers:
dim = 2 * self.n_hidden
else:
dim = 4 * self.n_hidden
return input_shape[0], dim, npatchesH, npatchesW
def get_output_for(self, input_var, **kwargs):
# HACK LASAGNE
# This is needed, jointly with the previous hack, to ensure that
# this layer behaves as its last sublayer (namely,
# self.input_layer)
return input_var
class BidirectionalRNNLayer(lasagne.layers.Layer):
# Setting a value for grad_clipping will clip the gradients in the layer
def __init__(
self,
l_in,
num_units,
RecurrentNet=lasagne.layers.GRULayer,
# common parameters
nonlinearity=lasagne.nonlinearities.rectify,
hid_init=lasagne.init.Constant(0.),
grad_clipping=0,
precompute_input=True,
mask_input=None,
# GRU specific params
gru_resetgate=lasagne.layers.Gate(W_cell=None),
gru_updategate=lasagne.layers.Gate(W_cell=None),
gru_hidden_update=lasagne.layers.Gate(
W_cell=None,
nonlinearity=lasagne.nonlinearities.tanh),
gru_hid_init=lasagne.init.Constant(0.),
batch_norm=False,
# LSTM specific params
lstm_ingate=lasagne.layers.Gate(),
lstm_forgetgate=lasagne.layers.Gate(),
lstm_cell=lasagne.layers.Gate(
W_cell=None,
nonlinearity=lasagne.nonlinearities.tanh),
lstm_outgate=lasagne.layers.Gate(),
# RNN specific params
rnn_W_in_to_hid=lasagne.init.Uniform(),
rnn_W_hid_to_hid=lasagne.init.Uniform(),
rnn_b=lasagne.init.Constant(0.),
name='',
**kwargs):
"""A Bidirectional RNN Layer
Parameters
----------
l_in : lasagne.layers.Layer
The input layer
num_units : int
The number of hidden units of each RNN
RecurrentNet : lasagne.layers.Layer
A recurrent layer class
nonlinearity : callable or None
The nonlinearity that is applied to the output. If
None is provided, no nonlinearity will be applied. Only for
LSTMLayer and RecurrentLayer
hid_init : callable, np.ndarray, theano.shared or
lasagne.layers.Layer
Initializer for initial hidden state
grad_clipping : float
If nonzero, the gradient messages are clipped to the given value
during the backward pass.
precompute_input : bool
If True, precompute input_to_hid before iterating through the
sequence. This can result in a speedup at the expense of an
increase in memory usage.
mask_input : lasagne.layers.Layer
Layer which allows for a sequence mask to be input, for when
sequences are of variable length. Default None, which means no mask
will be supplied (i.e. all sequences are of the same length).
gru_resetgate : lasagne.layers.Gate
Parameters for the reset gate, if RecurrentNet is GRU
gru_updategate : lasagne.layers.Gate
Parameters for the update gate, if RecurrentNet is GRU
gru_hidden_update : lasagne.layers.Gate
Parameters for the hidden update, if RecurrentNet is GRU
gru_hid_init : callable, np.ndarray, theano.shared or
lasagne.layers.Layer
Initializer for initial hidden state, if RecurrentNet is GRU
lstm_ingate : lasagne.layers.Gate
Parameters for the input gate, if RecurrentNet is LSTM
lstm_forgetgate : lasagne.layers.Gate
Parameters for the forget gate, if RecurrentNet is LSTM
lstm_cell : lasagne.layers.Gate
Parameters for the cell computation, if RecurrentNet is LSTM
lstm_outgate : lasagne.layers.Gate
Parameters for the output gate, if RecurrentNet is LSTM
rnn_W_in_to_hid : Theano shared variable, numpy array or callable
Initializer for input-to-hidden weight matrix, if
RecurrentNet is RecurrentLayer
rnn_W_hid_to_hid : Theano shared variable, numpy array or callable
Initializer for hidden-to-hidden weight matrix, if
RecurrentNet is RecurrentLayer
rnn_b : Theano shared variable, numpy array, callable or None
Initializer for bias vector, if RecurrentNet is
RecurrentLaye. If None is provided there will be no bias
name = string
The name of the layer, optional
"""
super(BidirectionalRNNLayer, self).__init__(l_in, name, **kwargs)
self.l_in = l_in
self.num_units = num_units
self.grad_clipping = grad_clipping
self.name = name
# We use a bidirectional RNN, which means we combine two
# RecurrentLayers, the second of which with backwards=True
# Setting only_return_final=True makes the layers only return their
# output for the final time step, which is all we need for this task
# GRU
if RecurrentNet.__name__ == 'GRULayer':
if batch_norm:
RecurrentNet = lasagne.layers.BNGRULayer
rnn_params = dict(
resetgate=gru_resetgate,
updategate=gru_updategate,
hidden_update=gru_hidden_update,
hid_init=gru_hid_init)
# LSTM
elif RecurrentNet.__name__ == 'LSTMLayer':
rnn_params = dict(
nonlinearity=nonlinearity,
ingate=lstm_ingate,
forgetgate=lstm_forgetgate,
cell=lstm_cell,
outgate=lstm_outgate)
# RNN
elif RecurrentNet.__name__ == 'RecurrentLayer':
rnn_params = dict(
nonlinearity=nonlinearity,
W_in_to_hid=rnn_W_in_to_hid,
W_hid_to_hid=rnn_W_hid_to_hid,
b=rnn_b)
else:
raise NotImplementedError('RecurrentNet not implemented')
common_params = dict(
hid_init=hid_init,
grad_clipping=grad_clipping,
precompute_input=precompute_input,
mask_input=mask_input,
only_return_final=False)
rnn_params.update(common_params)
l_forward = RecurrentNet(
l_in,
num_units,
name=name + '_l_forward_sub',
**rnn_params)
l_backward = RecurrentNet(
l_forward,
num_units,
backwards=True,
name=name + '_l_backward_sub',
**rnn_params)
# Now we'll concatenate the outputs to combine them
# Note that l_backward is already inverted by Lasagne
l_concat = lasagne.layers.ConcatLayer([l_forward, l_backward],
axis=2, name=name+'_concat')
# HACK LASAGNE
# This will set `self.input_layer`, which is needed by Lasagne to find
# the layers with the get_all_layers() helper function in the
# case of a layer with sublayers
if isinstance(l_concat, tuple):
self.input_layer = None
else:
self.input_layer = l_concat
def get_output_shape_for(self, input_shape):
return list(input_shape[0:2]) + [self.num_units * 2]
def get_output_for(self, input_var, **kwargs):
# HACK LASAGNE
# This is needed, jointly with the previous hack, to ensure that
# this layer behaves as its last sublayer (namely,
# self.input_layer)
return input_var
class LinearUpsamplingLayer(lasagne.layers.Layer):
def __init__(self,
incoming,
expand_height,
expand_width,
nclasses,
W=lasagne.init.Normal(0.01),
b=lasagne.init.Constant(.0),
batch_norm=False,
**kwargs):
super(LinearUpsamplingLayer, self).__init__(incoming, **kwargs)
nfeatures_in = self.input_shape[-1]
nfeatures_out = expand_height * expand_width * nclasses
self.nfeatures_out = nfeatures_out
self.incoming = incoming
self.expand_height = expand_height
self.expand_width = expand_width
self.nclasses = nclasses
self.batch_norm = batch_norm
# ``regularizable`` and ``trainable`` by default
self.W = self.add_param(W, (nfeatures_in, nfeatures_out), name='W')
if not batch_norm:
self.b = self.add_param(b, (nfeatures_out,), name='b')
def get_output_for(self, input_arr, **kwargs):
# upsample
pred = T.dot(input_arr, self.W)
if not self.batch_norm:
pred += self.b
nrows, ncolumns = self.input_shape[1:3]
batch_size = -1
nclasses = self.nclasses
expand_height = self.expand_height
expand_width = self.expand_width
# Reshape after the upsampling to come back to the original
# dimensions and move the pixels in the right place
pred = pred.reshape((batch_size,
nrows,
ncolumns,
expand_height,
expand_width,
nclasses))
pred = pred.dimshuffle((0, 1, 4, 2, 5, 3))
pred = pred.reshape((batch_size,
nrows * expand_height,
ncolumns * expand_width,
nclasses))
return pred
def get_output_shape_for(self, input_shape):
return (input_shape[0],
input_shape[1] * self.expand_height,
input_shape[2] * self.expand_width,
self.nclasses)
class CropLayer(lasagne.layers.Layer):
def __init__(self, l_in, crop, data_format='bc01', centered=True,
**kwargs):
super(CropLayer, self).__init__(l_in, crop, **kwargs)
assert data_format in ['bc01', 'b01c']
if not isinstance(crop, T.TensorVariable):
crop = lasagne.utils.as_tuple(crop, 2)
self.crop = crop
self.data_format = data_format
self.centered = centered
def get_output_shape_for(self, input_shape, **kwargs):
# self.crop is a tensor --> we cannot know in advance how much
# we will crop
if isinstance(self.crop, T.TensorVariable):
if self.data_format == 'bc01':
input_shape = list(input_shape)
input_shape[2] = None
input_shape[3] = None
else:
input_shape = list(input_shape)
input_shape[1] = None
input_shape[2] = None
# self.crop is a list of ints
else:
if self.data_format == 'bc01':
input_shape = list(input_shape)
input_shape[2] -= self.crop[0]
input_shape[3] -= self.crop[1]
else:
input_shape = list(input_shape)
input_shape[1] -= self.crop[0]
input_shape[2] -= self.crop[1]
return input_shape
def get_output_for(self, input_arr, **kwargs):
crop = self.crop.astype('int32') # Indices have to be int
sz = input_arr.shape
if self.data_format == 'bc01':
if self.centered:
idx0 = T.switch(T.eq(-crop[0] + crop[0]/2, 0),
sz[2], -crop[0] + crop[0]/2)
idx1 = T.switch(T.eq(-crop[1] + crop[1]/2, 0),
sz[3], -crop[1] + crop[1]/2)
return input_arr[:, :, crop[0]/2:idx0, crop[1]/2:idx1]
else:
idx0 = T.switch(T.eq(crop[0], 0), sz[2], -crop[0])
idx1 = T.switch(T.eq(crop[1], 0), sz[3], -crop[1])
return input_arr[:, :, :idx0, :idx1]
else:
if self.centered:
idx0 = T.switch(T.eq(-crop[0] + crop[0]/2, 0),
sz[1], -crop[0] + crop[0]/2)
idx1 = T.switch(T.eq(-crop[1] + crop[1]/2, 0),
sz[2], -crop[1] + crop[1]/2)
return input_arr[:, crop[0]/2:idx0, crop[1]/2:idx1, :]
else:
idx0 = T.switch(T.eq(crop[0], 0), sz[1], -crop[0])
idx1 = T.switch(T.eq(crop[1], 0), sz[2], -crop[1])
return input_arr[:, :idx0, :idx1, :]
================================================
FILE: padded.py
================================================
import warnings
import numpy
import lasagne
from lasagne import init, nonlinearities
from lasagne.layers import get_all_layers, Conv2DLayer, Layer, Pool2DLayer
import theano
from theano import tensor as T
from theano.ifelse import ifelse
class PaddedConv2DLayer(Conv2DLayer):
def __init__(self, incoming, num_filters, filter_size, stride=(1, 1),
pad=0, untie_biases=False, W=init.GlorotUniform(),
b=init.Constant(0.), nonlinearity=nonlinearities.rectify,
flip_filters=True, convolution=theano.tensor.nnet.conv2d,
centered=True, **kwargs):
"""A padded convolutional layer
Note
----
If used in place of a :class:``lasagne.layers.Conv2DLayer`` be
sure to specify `flag_filters=False`, which is the default for
that layer
Parameters
----------
incoming : lasagne.layers.Layer
The input layer
num_filters : int
The number of filters or kernels of the convolution
filter_size : int or iterable of int
The size of the filters
stride : int or iterable of int
The stride or subsampling of the convolution
pad : int, iterable of int, ``full``, ``same`` or ``valid``
**Ignored!** Kept for compatibility with the
:class:``lasagne.layers.Conv2DLayer``
untie_biases : bool
See :class:``lasagne.layers.Conv2DLayer``
W : Theano shared variable, expression, numpy array or callable
See :class:``lasagne.layers.Conv2DLayer``
b : Theano shared variable, expression, numpy array, callable or None
See :class:``lasagne.layers.Conv2DLayer``
nonlinearity : callable or None
See :class:``lasagne.layers.Conv2DLayer``
flip_filters : bool
See :class:``lasagne.layers.Conv2DLayer``
convolution : callable
See :class:``lasagne.layers.Conv2DLayer``
centered : bool
If True, the padding will be added on both sides. If False
the zero padding will be applied on the upper left side.
**kwargs
Any additional keyword arguments are passed to the
:class:``lasagne.layers.Layer`` superclass
"""
self.centered = centered
if pad not in [0, (0, 0), [0, 0]]:
warnings.warn('The specified padding will be ignored',
RuntimeWarning)
super(PaddedConv2DLayer, self).__init__(incoming, num_filters,
filter_size, stride, pad,
untie_biases, W, b,
nonlinearity, flip_filters,
**kwargs)
if self.input_shape[2:] != (None, None):
warnings.warn('This Layer should only be used when the size of '
'the image is not known', RuntimeWarning)
def get_output_for(self, input_arr, **kwargs):
# Compute the padding required not to crop any pixel
input_arr, pad = zero_pad(
input_arr, self.filter_size, self.stride, self.centered, 'bc01')
# Erase self.pad to prevent theano from padding the input
self.pad = 0
ret = super(PaddedConv2DLayer, self).get_output_for(input_arr,
**kwargs)
# Set pad to access it from outside
self.pad = pad
return ret
def get_output_shape_for(self, input_shape):
return zero_pad_shape(input_shape, self.filter_size, self.stride,
'bc01')
def get_equivalent_input_padding(self, layers_args=[]):
"""Compute the equivalent padding in the input layer
See :func:`padded.get_equivalent_input_padding`
"""
return(get_equivalent_input_padding(self, layers_args))
class PaddedPool2DLayer(Pool2DLayer):
def __init__(self, incoming, pool_size, stride=None, pad=(0, 0),
ignore_border=True, centered=True, **kwargs):
"""A padded pooling layer
Parameters
----------
incoming : lasagne.layers.Layer
The input layer
pool_size : int
The size of the pooling
stride : int or iterable of int
The stride or subsampling of the convolution
pad : int, iterable of int, ``full``, ``same`` or ``valid``
**Ignored!** Kept for compatibility with the
:class:``lasagne.layers.Pool2DLayer``
ignore_border : bool
See :class:``lasagne.layers.Pool2DLayer``
centered : bool
If True, the padding will be added on both sides. If False
the zero padding will be applied on the upper left side.
**kwargs
Any additional keyword arguments are passed to the Layer
superclass
"""
self.centered = centered
if pad not in [0, (0, 0), [0, 0]]:
warnings.warn('The specified padding will be ignored',
RuntimeWarning)
super(PaddedPool2DLayer, self).__init__(incoming,
pool_size,
stride,
pad,
ignore_border,
**kwargs)
if self.input_shape[2:] != (None, None):
warnings.warn('This Layer should only be used when the size of '
'the image is not known', RuntimeWarning)
def get_output_for(self, input_arr, **kwargs):
# Compute the padding required not to crop any pixel
input_arr, pad = zero_pad(
input_arr, self.pool_size, self.stride, self.centered, 'bc01')
# Erase self.pad to prevent theano from padding the input
self.pad = 0
ret = super(PaddedConv2DLayer, self).convolve(input_arr, **kwargs)
# Set pad to access it from outside
self.pad = pad
return ret
def get_output_shape_for(self, input_shape):
return zero_pad_shape(input_shape, self.pool_size, self.stride,
'bc01')
def get_equivalent_input_padding(self, layers_args=[]):
"""Compute the equivalent padding in the input layer
See :func:`padded.get_equivalent_input_padding`
"""
return(get_equivalent_input_padding(self, layers_args))
class DynamicPaddingLayer(Layer):
def __init__(
self,
l_in,
patch_size,
stride,
data_format='bc01',
centered=True,
name='',
**kwargs):
"""A Layer that zero-pads the input
Parameters
----------
l_in : lasagne.layers.Layer
The input layer
patch_size : iterable of int
The patch size
stride : iterable of int
The stride
data_format : string
The format of l_in, either `b01c` (batch, rows, cols,
channels) or `bc01` (batch, channels, rows, cols)
centered : bool
If True, the padding will be added on both sides. If False
the zero padding will be applied on the upper left side.
name = string
The name of the layer, optional
"""
super(DynamicPaddingLayer, self).__init__(l_in, name, **kwargs)
self.l_in = l_in
self.patch_size = patch_size
self.stride = stride
self.data_format = data_format
self.centered = centered
self.name = name
def get_output_for(self, input_arr, **kwargs):
input_arr, pad = zero_pad(
input_arr, self.patch_size, self.stride, self.centered,
self.data_format)
self.pad = pad
return input_arr
def get_output_shape_for(self, input_shape):
return zero_pad_shape(input_shape, self.patch_size, self.stride,
self.data_format, True)
def zero_pad(input_arr, patch_size, stride, centered=True, data_format='bc01'):
assert data_format in ['bc01', 'b01c']
if data_format == 'b01c':
in_shape = input_arr.shape[1:3]
else:
in_shape = input_arr.shape[2:] # bs, ch, rows, cols
in_shape -= patch_size
pad = in_shape % stride
pad = (stride - pad) % stride
# TODO improve efficiency by allocating the full array of zeros and
# setting the subtensor afterwards
if data_format == 'bc01':
if centered:
input_arr = ifelse(
T.eq(pad[0], 0),
input_arr,
T.concatenate(
(T.zeros_like(input_arr[:, :, :pad[0]/2, :]),
input_arr,
T.zeros_like(input_arr[:, :, :pad[0] - pad[0]/2, :])),
2))
input_arr = ifelse(
T.eq(pad[1], 0),
input_arr,
T.concatenate(
(T.zeros_like(input_arr[:, :, :, :pad[1]/2]),
input_arr,
T.zeros_like(input_arr[:, :, :, :pad[1] - pad[1]/2])),
3))
else:
input_arr = ifelse(
T.eq(pad[0], 0),
input_arr,
T.concatenate((T.zeros_like(input_arr[:, :, :pad[0], :]),
input_arr), 2))
input_arr = ifelse(
T.eq(pad[1], 0),
input_arr,
T.concatenate((T.zeros_like(input_arr[:, :, :, :pad[1]]),
input_arr), 3))
else:
if centered:
input_arr = ifelse(
T.eq(pad[0], 0),
input_arr,
T.concatenate(
(T.zeros_like(input_arr[:, :pad[0]/2, :, :]),
input_arr,
T.zeros_like(input_arr[:, :pad[0] - pad[0]/2, :, :])),
1))
input_arr = ifelse(
T.eq(pad[1], 0),
input_arr,
T.concatenate(
(T.zeros_like(input_arr[:, :, :pad[1]/2, :]),
input_arr,
T.zeros_like(input_arr[:, :, :pad[1] - pad[1]/2, :])),
2))
else:
input_arr = ifelse(
T.eq(pad[0], 0),
input_arr,
T.concatenate((T.zeros_like(input_arr[:, :pad[0], :, :]),
input_arr), 1))
input_arr = ifelse(
T.eq(pad[1], 0),
input_arr,
T.concatenate((T.zeros_like(input_arr[:, :, :pad[1], :]),
input_arr), 2))
return input_arr, pad
def zero_pad_shape(input_shape, patch_size, stride, data_format,
only_pad=False):
assert data_format in ['bc01', 'b01c']
patch_size = numpy.array(patch_size)
stride = numpy.array(stride)
if data_format == 'b01c':
im_shape = numpy.array(input_shape[1:3])
else:
im_shape = numpy.array(input_shape[2:])
pad = (im_shape - patch_size) % stride
pad = (stride - pad) % stride
if only_pad:
out_shape = list(im_shape + pad)
else:
out_shape = list((im_shape - patch_size + pad) / stride + 1)
if data_format == 'b01c':
out_shape = [input_shape[0]] + out_shape + [input_shape[3]]
else:
out_shape = list(input_shape[:2]) + out_shape
return list(out_shape)
def get_equivalent_input_padding(layer, layers_args=[]):
"""Compute the equivalent padding in the input layer
A function to compute the equivalent padding of a sequence of
convolutional and pooling layers. It memorizes the padding
of all the Layers up to the first InputLayer.
It then computes what would be the equivalent padding in the Layer
immediately before the chain of Layers that is being taken into account.
"""
# Initialize the DynamicPadding layers
lasagne.layers.get_output(layer)
# Loop through conv and pool to collect data
all_layers = get_all_layers(layer)
# while(not isinstance(layer, (InputLayer))):
for layer in all_layers:
# Note: stride is numerical, but pad *could* be symbolic
try:
pad, stride = (layer.pad, layer.stride)
if isinstance(pad, int):
pad = pad, pad
if isinstance(stride, int):
stride = stride, stride
layers_args.append((pad, stride))
except(AttributeError):
pass
# Loop backward to compute the equivalent padding in the input
# layer
tot_pad = T.zeros(2)
pad_factor = T.ones(2)
while(layers_args):
pad, stride = layers_args.pop()
tot_pad += pad * pad_factor
pad_factor *= stride
return tot_pad
================================================
FILE: reseg.py
================================================
# Standard library imports
import cPickle as pkl
import collections
import os
import random
from shutil import move, rmtree
import sys
import time
# Related third party imports
import lasagne
from lasagne.layers import get_output
import numpy as np
from progressbar import ProgressBar
import theano
from theano import tensor as T
from theano.compile.nanguardmode import NanGuardMode
# Local application/library specific imports
from helper_dataset import preprocess_dataset
from get_info_model import print_params
from layers import CropLayer, ReSegLayer
from subprocess import check_output
from utils import iterate_minibatches, save_with_retry, validate, VariableText
# Datasets import
# TODO these should go into preprocess/helper dataset/evaluate
import camvid
floatX = theano.config.floatX
intX = 'uint8'
debug = False
nanguard = False
datasets = {'camvid': (camvid.load_data, camvid.properties)}
def get_dataset(name):
return (datasets[name][0], datasets[name][1])
def buildReSeg(input_shape, input_var,
n_layers, pheight, pwidth, dim_proj,
nclasses, stack_sublayers,
# upsampling
out_upsampling,
out_nfilters,
out_filters_size,
out_filters_stride,
out_W_init=lasagne.init.GlorotUniform(),
out_b_init=lasagne.init.Constant(0.),
out_nonlinearity=lasagne.nonlinearities.rectify,
# input ConvLayers
in_nfilters=None,
in_filters_size=(),
in_filters_stride=(),
in_W_init=lasagne.init.GlorotUniform(),
in_b_init=lasagne.init.Constant(0.),
in_nonlinearity=lasagne.nonlinearities.rectify,
# common recurrent layer params
RecurrentNet=lasagne.layers.GRULayer,
nonlinearity=lasagne.nonlinearities.rectify,
hid_init=lasagne.init.Constant(0.),
grad_clipping=0,
precompute_input=True,
mask_input=None,
# 1x1 Conv layer for dimensional reduction
conv_dim_red=False,
conv_dim_red_nonlinearity=lasagne.nonlinearities.identity,
# GRU specific params
gru_resetgate=lasagne.layers.Gate(W_cell=None),
gru_updategate=lasagne.layers.Gate(W_cell=None),
gru_hidden_update=lasagne.layers.Gate(
W_cell=None,
nonlinearity=lasagne.nonlinearities.tanh),
gru_hid_init=lasagne.init.Constant(0.),
# LSTM specific params
lstm_ingate=lasagne.layers.Gate(),
lstm_forgetgate=lasagne.layers.Gate(),
lstm_cell=lasagne.layers.Gate(
W_cell=None,
nonlinearity=lasagne.nonlinearities.tanh),
lstm_outgate=lasagne.layers.Gate(),
# RNN specific params
rnn_W_in_to_hid=lasagne.init.Uniform(),
rnn_W_hid_to_hid=lasagne.init.Uniform(),
rnn_b=lasagne.init.Constant(0.),
# Special layer
batch_norm=False
):
'''Helper function to build a ReSeg network'''
# Input is b01c
print('Input shape: ' + str(input_shape))
l_in = lasagne.layers.InputLayer(shape=input_shape,
input_var=input_var,
name="input_layer")
# Convert to bc01 (batchsize, ch, rows, cols)
l_in = lasagne.layers.DimshuffleLayer(l_in, (0, 3, 1, 2))
# To know the upsampling ratio we compute what is the feature map
# size at the end of the downsampling pathway for an hypotetical
# initial size of 100 (we just need the ratio, so we don't care
# about the actual size)
hypotetical_fm_size = np.array((100.0, 100.0))
l_reseg = ReSegLayer(l_in, n_layers, pheight, pwidth, dim_proj,
nclasses, stack_sublayers,
# upsampling
out_upsampling,
out_nfilters,
out_filters_size,
out_filters_stride,
out_W_init=out_W_init,
out_b_init=out_b_init,
out_nonlinearity=out_nonlinearity,
hypotetical_fm_size=hypotetical_fm_size,
# input ConvLayers
in_nfilters=in_nfilters,
in_filters_size=in_filters_size,
in_filters_stride=in_filters_stride,
in_W_init=in_W_init,
in_b_init=in_b_init,
in_nonlinearity=in_nonlinearity,
# common recurrent layer params
RecurrentNet=RecurrentNet,
nonlinearity=nonlinearity,
hid_init=hid_init,
grad_clipping=grad_clipping,
precompute_input=precompute_input,
mask_input=mask_input,
# 1x1 Conv layer for dimensional reduction
conv_dim_red=conv_dim_red,
conv_dim_red_nonlinearity=conv_dim_red_nonlinearity,
# GRU specific params
gru_resetgate=gru_resetgate,
gru_updategate=gru_updategate,
gru_hidden_update=gru_hidden_update,
gru_hid_init=gru_hid_init,
# LSTM specific params
lstm_ingate=lstm_ingate,
lstm_forgetgate=lstm_forgetgate,
lstm_cell=lstm_cell,
lstm_outgate=lstm_outgate,
# RNN specific params
rnn_W_in_to_hid=rnn_W_in_to_hid,
rnn_W_hid_to_hid=rnn_W_hid_to_hid,
rnn_b=rnn_b,
# Special layers
batch_norm=batch_norm,
name='reseg')
# Dynamic cropping
target_size = get_output(l_in).shape[2:]
crop = get_output(l_reseg).shape[2:] - target_size
l_out = CropLayer(l_reseg, crop, centered=False)
# channel = nclasses
if 'linear' not in out_upsampling:
l_out = lasagne.layers.Conv2DLayer(
l_out,
num_filters=nclasses,
filter_size=(1, 1),
stride=(1, 1),
W=out_W_init,
b=out_b_init,
nonlinearity=None
)
if batch_norm:
l_out = lasagne.layers.batch_norm(l_out, axes='auto')
# Go to b01c
l_out = lasagne.layers.DimshuffleLayer(
l_out,
[0, 2, 3, 1],
name='dimshuffle_before_softmax')
# Reshape in 2D, last dimension is nclasses, where the softmax is applied
l_out_shape = get_output(l_out).shape
l_out = lasagne.layers.ReshapeLayer(
l_out,
(T.prod(l_out_shape[0:3]), l_out_shape[3]),
name='reshape_before_softmax')
l_out = lasagne.layers.NonlinearityLayer(
l_out,
nonlinearity=lasagne.nonlinearities.softmax,
name="softmax_layer")
return l_out
def getFunctions(input_var, target_var, class_balance_w_var, l_pred,
batch_norm=False, weight_decay=0.,
optimizer=lasagne.updates.adadelta,
learning_rate=None, momentum=None,
rho=None, beta1=None, beta2=None, epsilon=None, ):
'''Helper function to build the training function
'''
input_shape = input_var.shape
# Compute BN params for prediction
batch_norm_params = dict()
if batch_norm:
batch_norm_params.update(
dict(batch_norm_update_averages=False))
batch_norm_params.update(
dict(batch_norm_use_averages=True))
# Prediction function:
# computes the deterministic distribution over the labels, i.e. we
# disable the stochastic layers such as Dropout
prediction = lasagne.layers.get_output(l_pred, deterministic=True,
**batch_norm_params)
f_pred = theano.function(
[input_var],
T.argmax(prediction, axis=1).reshape(
(-1, input_shape[1], input_shape[2])))
# Compute the loss to be minimized during training
batch_norm_params = dict()
if batch_norm:
batch_norm_params.update(
dict(batch_norm_update_averages=True))
batch_norm_params.update(
dict(batch_norm_use_averages=False))
prediction = lasagne.layers.get_output(l_pred,
**batch_norm_params)
loss = lasagne.objectives.categorical_crossentropy(
prediction, target_var)
loss *= class_balance_w_var
loss = loss.reshape((-1, input_shape[1] * input_shape[2]))
# Compute the cumulative loss (over the pixels) per minibatch
loss = T.sum(loss, axis=1)
# Compute the mean loss
loss = T.mean(loss, axis=0)
if weight_decay > 0:
l2_penalty = lasagne.regularization.regularize_network_params(
l_pred,
lasagne.regularization.l2,
tags={'regularizable': True})
loss += l2_penalty * weight_decay
params = lasagne.layers.get_all_params(l_pred, trainable=True)
opt_params = dict()
if optimizer.__name__ == 'sgd':
if learning_rate is None:
raise TypeError("Learning rate can't be 'None' with SGD")
opt_params = dict(learning_rate=learning_rate)
elif (optimizer.__name__ == 'momentum' or
optimizer.__name__ == 'nesterov_momentum'):
if learning_rate is None:
raise TypeError("Learning rate can't be 'None' "
"with Momentum SGD or Nesterov Momentum")
opt_params = dict(
learning_rate=learning_rate,
momentum=momentum
)
elif optimizer.__name__ == 'adagrad':
if learning_rate is not None:
opt_params.update(dict(learning_rate=learning_rate))
if epsilon is not None:
opt_params.update(dict(epsilon=epsilon))
elif (optimizer.__name__ == 'rmsprop' or
optimizer.__name__ == 'adadelta'):
if learning_rate is not None:
opt_params.update(dict(learning_rate=learning_rate))
if rho is not None:
opt_params.update(dict(rho=rho))
if epsilon is not None:
opt_params.update(dict(epsilon=epsilon))
elif (optimizer.__name__ == 'adam' or
optimizer.__name__ == 'adamax'):
if learning_rate is not None:
opt_params.update(dict(learning_rate=learning_rate))
if beta1 is not None:
opt_params.update(dict(beta1=beta1))
if beta2 is not None:
opt_params.update(dict(beta2=beta2))
if epsilon is not None:
opt_params.update(dict(epsilon=epsilon))
else:
raise NotImplementedError('Optimization method not implemented')
updates = optimizer(loss, params, **opt_params)
# Training function:
# computes the training loss (with stochasticity, if any) and
# updates the weights using the updates dictionary provided by the
# optimization function
f_train = theano.function([input_var, target_var, class_balance_w_var],
loss, updates=updates)
return f_pred, f_train
def train(saveto='model.npz',
tmp_saveto=None,
# Input Conv layers
in_nfilters=None, # None = no input convolution
in_filters_size=(),
in_filters_stride=(),
in_W_init=lasagne.init.GlorotUniform(),
in_b_init=lasagne.init.Constant(0.),
in_nonlinearity=lasagne.nonlinearities.rectify,
# RNNs layers
dim_proj=[32, 32],
pwidth=2,
pheight=2,
stack_sublayers=(True, True),
RecurrentNet=lasagne.layers.GRULayer,
nonlinearity=lasagne.nonlinearities.rectify,
hid_init=lasagne.init.Constant(0.),
grad_clipping=0,
precompute_input=True,
mask_input=None,
# 1x1 Conv layer for dimensional reduction
conv_dim_red=False,
conv_dim_red_nonlinearity=lasagne.nonlinearities.identity,
# GRU specific params
gru_resetgate=lasagne.layers.Gate(W_cell=None),
gru_updategate=lasagne.layers.Gate(W_cell=None),
gru_hidden_update=lasagne.layers.Gate(
W_cell=None,
nonlinearity=lasagne.nonlinearities.tanh),
gru_hid_init=lasagne.init.Constant(0.),
# LSTM specific params
lstm_ingate=lasagne.layers.Gate(),
lstm_forgetgate=lasagne.layers.Gate(),
lstm_cell=lasagne.layers.Gate(
W_cell=None,
nonlinearity=lasagne.nonlinearities.tanh),
lstm_outgate=lasagne.layers.Gate(),
# RNN specific params
rnn_W_in_to_hid=lasagne.init.Uniform(),
rnn_W_hid_to_hid=lasagne.init.Uniform(),
rnn_b=lasagne.init.Constant(0.),
# Output upsampling layers
out_upsampling='grad',
out_nfilters=None, # The last number should be the num of classes
out_filters_size=(1, 1),
out_filters_stride=None,
out_W_init=lasagne.init.GlorotUniform(),
out_b_init=lasagne.init.Constant(0.),
out_nonlinearity=lasagne.nonlinearities.rectify,
# Prediction, Softmax
intermediate_pred=None,
class_balance=None,
# Special layers
batch_norm=False,
use_dropout=False,
dropout_rate=0.5,
use_dropout_x=False,
dropout_x_rate=0.8,
# Optimization method
optimizer=lasagne.updates.adadelta,
learning_rate=None,
momentum=None,
rho=None,
beta1=None,
beta2=None,
epsilon=None,
weight_decay=0., # l2 reg
weight_noise=0.,
# Early stopping
patience=500, # Num updates with no improvement before early stop
max_epochs=5000,
min_epochs=100,
# Sampling and validation params
validFreq=1000,
saveFreq=1000, # Parameters pickle frequency
n_save=-1, # If n_save is a list of indexes, the corresponding
# elements of each split are saved. If n_save is an
# integer, n_save random elements for each split are
# saved. If n_save is -1, all the dataset is saved
valid_wait=0,
# Batch params
batch_size=8,
valid_batch_size=1,
shuffle=True,
# Dataset
dataset='horses',
color_space='RGB',
color=True,
use_depth=None,
resize_images=True,
resize_size=-1,
# Pre-processing
preprocess_type=None,
patch_size=(9, 9),
max_patches=1e5,
# Data augmentation
do_random_flip=False,
do_random_shift=False,
do_random_invert_color=False,
shift_pixels=2,
reload_=False
):
# Set options and history_acc
# ----------------------------
start = time.time() # we use time.time() to know the *real-world* time
bestparams = {}
rng = np.random.RandomState(0xbeef)
saveto = [tmp_saveto, saveto] if tmp_saveto else [saveto]
if type(pwidth) != list:
pwidth = [pwidth] * len(dim_proj)
if type(pheight) != list:
pheight = [pheight] * len(dim_proj)
# TODO Intermediate pred should probably have length nlayer - 1,
# i.e., we don't need to enforce the last one to be True
# TODO We are not using it for now
# if intermediate_pred is None:
# intermediate_pred = [[False] * (len(dim_proj) - 1)] + [[False, True]]
# if not unroll(intermediate_pred)[-1]:
# raise ValueError('The last value of intermediate_pred should be True')
if not resize_images and valid_batch_size != 1:
raise ValueError('When images are not resized valid_batch_size'
'should be 1')
color = color if color else False
nchannels = 3 if color else 1
mode = None
if nanguard:
mode = NanGuardMode(nan_is_error=True, inf_is_error=True,
big_is_error=True)
options = locals().copy()
# Repositories hash
options['recseg_version'] = check_output('git rev-parse HEAD',
shell=True)[:-1]
options['lasagne_version'] = lasagne.__version__
options['theano_version'] = theano.__version__
# options['trng'] = [el[0].get_value() for el in trng.state_updates]
options['history_acc'] = np.array([])
options['history_conf_matrix'] = np.array([])
options['history_iou_index'] = np.array([])
options['eidx'] = 0
options['uidx'] = 0
# Reload
# ------
if reload_:
for s in saveto[::-1]:
try:
with open('%s.pkl' % s, 'rb') as f:
options_reloaded = pkl.load(f)
for k, v in options.iteritems():
if k in ['trng', 'history_acc',
'history_conf_matrix',
'history_iou_index']:
continue
if k not in options_reloaded:
print('{} was not present in the options '
'file'.format(k))
options_reloaded[k] = v
options = options_reloaded
print('Option file loaded: {}'.format(s))
break
except IOError:
continue
saveto = options['saveto']
# Input Conv layers
in_nfilters = options['in_nfilters']
in_filters_size = options['in_filters_size']
in_filters_stride = options['in_filters_stride']
in_W_init = options['in_W_init']
in_b_init = options['in_b_init']
in_nonlinearity = options['in_nonlinearity']
# RNNs layers
dim_proj = options['dim_proj']
pwidth = options['pwidth']
pheight = options['pheight']
stack_sublayers = options['stack_sublayers']
RecurrentNet = options['RecurrentNet']
nonlinearity = options['nonlinearity']
hid_init = options['hid_init']
grad_clipping = options['grad_clipping']
precompute_input = options['precompute_input']
mask_input = options['mask_input']
# 1x1 Conv layer for dimensional reduction
conv_dim_red = options['conv_dim_red']
conv_dim_red_nonlinearity = options['conv_dim_red_nonlinearity']
# GRU specific params
gru_resetgate = options['gru_resetgate']
gru_updategate = options['gru_updategate']
gru_hidden_update = options['gru_hidden_update']
gru_hid_init = options['gru_hid_init']
# LSTM specific params
lstm_ingate = options['lstm_ingate']
lstm_forgetgate = options['lstm_forgetgate']
lstm_cell = options['lstm_cell']
lstm_outgate = options['lstm_outgate']
# RNN specific params
rnn_W_in_to_hid = options['rnn_W_in_to_hid']
rnn_W_hid_to_hid = options['rnn_W_hid_to_hid']
rnn_b = options['rnn_b']
# Output upsampling layers
out_upsampling = options['out_upsampling']
out_nfilters = options['out_nfilters']
out_filters_size = options['out_filters_size']
out_filters_stride = options['out_filters_stride']
out_W_init = options['out_W_init']
out_b_init = options['out_b_init']
out_nonlinearity = options['out_nonlinearity']
# Prediction, Softmax
intermediate_pred = options['intermediate_pred']
class_balance = options['class_balance']
valid_wait = options['valid_wait']
# Special layers
batch_norm = options['batch_norm']
use_dropout = options['use_dropout']
dropout_rate = options['dropout_rate']
use_dropout_x = options['use_dropout_x']
dropout_x_rate = options['dropout_x_rate']
# Optimization method
optimizer = options['optimizer']
learning_rate = options['learning_rate']
momentum = options['momentum']
rho = options['rho']
beta1 = options['beta1']
beta2 = options['beta2']
epsilon = options['epsilon']
weight_decay = options['weight_decay']
weight_noise = options['weight_noise']
# Batch params
batch_size = options['batch_size']
valid_batch_size = options['valid_batch_size']
shuffle = options['shuffle']
# Dataset
dataset = options['dataset']
color_space = options['color_space']
color = options['color']
use_depth = options['use_depth']
resize_images = options['resize_images']
resize_size = options['resize_size']
# Pre-processing
preprocess_type = options['preprocess_type']
patch_size = options['patch_size']
max_patches = options['max_patches']
# Data augmentation
do_random_flip = options['do_random_flip']
do_random_shift = options['do_random_shift']
do_random_invert_color = options['do_random_invert_color']
shift_pixels = options['shift_pixels']
# Save state from options
rng = options['rng']
# trng = options['trng'] --> to be reloaded after building the model
history_acc = options['history_acc'].tolist()
history_conf_matrix = options['history_conf_matrix'].tolist()
history_iou_index = options['history_iou_index'].tolist()
print_params(options)
n_layers = len(dim_proj)
assert class_balance in [None, 'median_freq_cost', 'natural_freq_cost',
'priors_correction'], (
'The balance class method is not implemented')
assert (preprocess_type in [None, 'f-whiten', 'conv-zca', 'sub-lcn',
'subdiv-lcn', 'gcn', 'local_mean_sub']), (
"The preprocessing method choosen is not implemented")
# Load data
# ---------
print("Loading data ...")
load_data, properties = get_dataset(dataset)
train, valid, test, mean, std, filenames, fullmasks = load_data(
resize_images=resize_images,
resize_size=resize_size,
color=color,
color_space=color_space,
rng=rng,
use_depth=use_depth,
with_filenames=True,
with_fullmasks=True)
has_void_class = properties()['has_void_class']
if not color:
if mean.ndim == 3:
mean = np.expand_dims(mean, axis=3)
if std.ndim == 3:
std = np.expand_dims(std, axis=3)
# Preprocess each image separately usually with LCN in order not to lose
# time at each epoch
# Default: input is float btw 0 and 1
# If we use vgg convnet the input should be 0:255
input_to_float = False if type(in_nfilters) == str else True
train, valid, test = preprocess_dataset(train, valid, test,
input_to_float,
preprocess_type,
patch_size, max_patches)
# Compute the indexes of the images to be saved
if isinstance(n_save, collections.Iterable):
samples_ids = np.array(n_save)
elif n_save != -1:
samples_ids = [
random.sample(range(len(s)), min(len(s), n_save)) for s in
[train[0], valid[0], test[0]]]
else:
samples_ids = [range(len(s)) for s in [train[0], valid[0], test[0]]]
options['samples_ids'] = samples_ids
# Retrieve basic size informations and split train variables
x_train, y_train = train
if len(x_train) == 0:
raise RuntimeError("Dataset not found")
filenames_train, filenames_valid, filenames_test = filenames
cheight, cwidth, cchannels = x_train[0].shape
nclasses = max([np.max(el) for el in y_train]) + 1
print '# of classes:', nclasses
# Remove the segmentation samples dir to make sure we don't mix samples
# from different experiments
seg_path = os.path.join('segmentations', dataset,
saveto[0].split('/')[-1][:-4])
try:
rmtree(seg_path)
except OSError:
pass
# Class balancing
# ---------------
# TODO: check if it works...
w_freq = 1
if class_balance in ['median_freq_cost', 'rare_freq_cost']:
u_train, c_train = np.unique(y_train, return_counts=True)
priors = c_train.astype(theano.config.floatX) / train[1].size
# the denominator is computed by summing the total number
# of pixels of the images where the class is present
# so it should be even more balanced
px_count = np.zeros(u_train.shape)
for tt in y_train:
u_tt = np.unique(tt)
px_t = tt.size
for uu in u_tt:
px_count[uu] += px_t
priors = c_train.astype(theano.config.floatX) / px_count
if class_balance == 'median_freq_cost':
w_freq = np.median(priors) / priors
elif class_balance == 'rare_freq_cost':
w_freq = 1 / (nclasses * priors)
print "Class balance weights", w_freq
assert len(priors) == nclasses, ("Number of computed priors are "
"different from number of classes")
if validFreq == -1:
validFreq = len(x_train)/batch_size
if saveFreq == -1:
saveFreq = len(x_train)/batch_size
# Model compilation
# -----------------
print("Building model ...")
input_shape = (None, cheight, cwidth, cchannels)
input_var = T.tensor4('inputs')
target_var = T.ivector('targets')
class_balance_w_var = T.vector('class_balance_w_var')
# Set the RandomStream to assure repeatability
lasagne.random.set_rng(rng)
# Tag test values
if debug:
print "DEBUG MODE: loading tag.test_value ..."
load_data, properties = get_dataset(dataset)
train, _, _, _, _ = load_data(
resize_images=resize_images, resize_size=resize_size,
color=color, color_space=color_space, rng=rng)
x_tag = (train[0][0:batch_size]).astype(floatX)
y_tag = (train[1][0:batch_size]).astype(intX)
# TODO Move preprocessing in a separate function
if x_tag.ndim == 1:
x_tag = x_tag[0]
y_tag = y_tag[0]
if x_tag.ndim == 3:
x_tag = np.expand_dims(x_tag, 0)
y_tag = np.expand_dims(y_tag, 0)
input_var.tag.test_value = x_tag
target_var.tag.test_value = y_tag.flatten()
class_balance_w_var.tag.test_value = np.ones(
np.prod(x_tag.shape[:3])).astype(floatX)
theano.config.compute_test_value = 'warn'
# Build the model
l_out = buildReSeg(input_shape, input_var,
n_layers, pheight, pwidth,
dim_proj, nclasses, stack_sublayers,
# upsampling
out_upsampling,
out_nfilters,
out_filters_size,
out_filters_stride,
out_W_init=out_W_init,
out_b_init=out_b_init,
out_nonlinearity=out_nonlinearity,
# input ConvLayers
in_nfilters=in_nfilters,
in_filters_size=in_filters_size,
in_filters_stride=in_filters_stride,
in_W_init=in_W_init,
in_b_init=in_b_init,
in_nonlinearity=in_nonlinearity,
# common recurrent layer params
RecurrentNet=RecurrentNet,
nonlinearity=nonlinearity,
hid_init=hid_init,
grad_clipping=grad_clipping,
precompute_input=precompute_input,
mask_input=mask_input,
# 1x1 Conv layer for dimensional reduction
conv_dim_red=conv_dim_red,
conv_dim_red_nonlinearity=conv_dim_red_nonlinearity,
# GRU specific params
gru_resetgate=gru_resetgate,
gru_updategate=gru_updategate,
gru_hidden_update=gru_hidden_update,
gru_hid_init=gru_hid_init,
# LSTM specific params
lstm_ingate=lstm_ingate,
lstm_forgetgate=lstm_forgetgate,
lstm_cell=lstm_cell,
lstm_outgate=lstm_outgate,
# RNN specific params
rnn_W_in_to_hid=rnn_W_in_to_hid,
rnn_W_hid_to_hid=rnn_W_hid_to_hid,
rnn_b=rnn_b,
# special layers
batch_norm=batch_norm)
f_pred, f_train = getFunctions(input_var, target_var, class_balance_w_var,
l_out, weight_decay, optimizer=optimizer,
learning_rate=learning_rate,
momentum=momentum, rho=rho, beta1=beta1,
beta2=beta2, epsilon=epsilon)
# Reload the list of the value parameters
# TODO Check if the saved params are CudaNDArrays or not, so that we
# don't need a GPU to reload the model (I'll do it when you are
# done)
if reload_:
for s in saveto[::-1]:
try:
with np.load('%s' % s) as f:
vparams = [f['arr_%d' % i] for i in range(len(f.files))]
lastparams, bestparams = vparams
# for i, v in enumerate(options['trng']):
# trng.state_updates[i][0].set_value(v)
print('Model file loaded: {}'.format(s))
lasagne.layers.set_all_param_values(l_out, bestparams)
break
except IOError:
continue
# Main loop
# ---------
print("Starting training...")
uidx = options['uidx']
patience_counter = 0
estop = False
save = False
epochs_wid = VariableText(
'Epoch %(epoch)d/' + str(max_epochs) + ' Up %(up)d',
{'epoch': 0, 'up': 0})
metrics_wid = VariableText(
'Cost %(cost)f, DD %(DD)f, UD %(UD)f %(shape)s',
{'cost': 0,
'DD': 0,
'UD': 0,
'shape': 0})
widgets = [
'', epochs_wid,
' ', metrics_wid]
pbar = ProgressBar(widgets=widgets, maxval=len(x_train),
redirect_stdout=True).start()
# Epochs loop
for eidx in range(options['uidx'], max_epochs):
nsamples = 0
epoch_cost = 0
start_time = time.time()
# Minibatches loop
for i, minibatch in enumerate(iterate_minibatches(x_train,
y_train,
batch_size,
rng=rng,
shuffle=shuffle)):
inputs, targets, _ = minibatch
st = time.time()
nsamples += len(inputs)
uidx += 1
# otherwise the normalization has been done before the preprocess
# if preprocess_type is None:
# inputs = inputs.astype(floatX)
targets = targets.astype(intX)
targets_flat = targets.flatten()
dd = time.time() - st
st = time.time()
# Class balance
class_balance_w = np.ones(np.prod(inputs.shape[:3])).astype(floatX)
if class_balance in ['median_freq_cost', 'rare_freq_cost']:
class_balance_w = w_freq[targets_flat].astype(floatX)
# Compute cost
cost = f_train(inputs.astype(floatX), targets_flat,
class_balance_w)
ud = time.time() - st
if np.isnan(cost):
raise RuntimeError('NaN detected')
if np.isinf(cost):
raise RuntimeError('Inf detected')
# if np.mod(uidx, dispFreq) == 0:
# print('Epoch {}, Up {}, Cost {:.3f}, DD {:.3f}, UD ' +
# '{:.5f} {}').format(eidx, uidx, float(cost), dd, ud,
# input_shape)
epochs_wid.update_mapping({'epoch': eidx, 'up': uidx})
metrics_wid.update_mapping(
{'cost': float(cost),
'DD': dd,
'UD': ud,
'shape': input_shape})
pbar.update(min(i*batch_size + 1, len(x_train)))
def validate_model():
(train_global_acc,
train_conf_matrix,
train_mean_class_acc,
train_iou_index,
train_mean_iou_index) = validate(f_pred,
train,
valid_batch_size,
has_void_class,
preprocess_type,
nclasses,
samples_ids=samples_ids[0],
filenames=filenames_train,
folder_dataset='train',
dataset=dataset,
saveto=saveto[0])
(valid_global_acc,
valid_conf_matrix,
valid_mean_class_acc,
valid_iou_index,
valid_mean_iou_index) = validate(f_pred,
valid,
valid_batch_size,
has_void_class,
preprocess_type,
nclasses,
samples_ids=samples_ids[1],
filenames=filenames_valid,
folder_dataset='valid',
dataset=dataset,
saveto=saveto[0])
(test_global_acc,
test_conf_matrix,
test_mean_class_acc,
test_iou_index,
test_mean_iou_index) = validate(f_pred,
test,
valid_batch_size,
has_void_class,
preprocess_type,
nclasses,
samples_ids=samples_ids[2],
filenames=filenames_test,
folder_dataset='test',
dataset=dataset,
saveto=saveto[0])
print("")
print("Global Accuracies:")
print('Train {:.5f} Valid {:.5f} Test {:.5f}'.format(
train_global_acc, valid_global_acc, test_global_acc))
print('Mean Class Accuracy - Train {:.5f} Valid {:.5f} '
'Test {:.5f}'.format(train_mean_class_acc,
valid_mean_class_acc,
test_mean_class_acc))
print('Mean Class iou - Train {:.5f} Valid {:.5f} '
'Test {:.5f}'.format(train_mean_iou_index,
valid_mean_iou_index,
test_mean_iou_index))
print("")
history_acc.append([train_global_acc,
train_mean_class_acc,
train_mean_iou_index,
valid_global_acc,
valid_mean_class_acc,
valid_mean_iou_index,
test_global_acc,
test_mean_class_acc,
test_mean_iou_index])
history_conf_matrix.append([train_conf_matrix,
valid_conf_matrix,
test_conf_matrix])
history_iou_index.append([train_iou_index,
valid_iou_index,
test_iou_index])
options['history_acc'] = np.array(history_acc)
options['history_conf_matrix'] = np.array(history_conf_matrix)
options['history_iou_index'] = np.array(history_iou_index)
return valid_mean_iou_index, test_mean_iou_index
# Check predictions' accuracy
if np.mod(uidx, validFreq) == 0:
if valid_wait == 0:
(valid_mean_iou_index,
test_mean_iou_index) = validate_model()
# Did we improve *validation* mean IOU accuracy?
if (len(valid) > 0 and
(len(history_acc) == 0 or valid_mean_iou_index >=
np.array(history_acc)[:, 5].max())):
# TODO check if CUDA variables!
bestparams = lasagne.layers.get_all_param_values(l_out)
patience_counter = 0
save = True # Save model params
# Early stop if patience is over
if (eidx > min_epochs):
patience_counter += 1
if patience_counter == patience / validFreq:
print 'Early Stop!'
estop = True
else:
valid_wait -= 1
# Save model parameters
if save or np.mod(uidx, saveFreq) == 0:
save_time = time.time()
lastparams = lasagne.layers.get_all_param_values(l_out)
vparams = [lastparams, bestparams]
# Retry if filesystem is busy
save_with_retry(saveto[0], vparams)
save = False
pkl.dump(options,
open('%s.pkl' % saveto[0], 'wb'))
print 'Saved parameters and options in {} in {:.3f}s'.format(
saveto[0], time.time() - save_time)
epoch_cost += cost
# exit minibatches loop
if estop:
break
# exit epochs loop
if estop:
break
print("Epoch {} of {} took {:.3f}s with overall cost {:.3f}".format(
eidx + 1, max_epochs, time.time() - start_time, epoch_cost))
pbar.finish()
max_valid_idx = np.argmax(np.array(history_acc)[:, 5])
best = history_acc[max_valid_idx]
(train_global_acc,
train_mean_class_acc,
train_mean_iou_index,
valid_global_acc,
valid_mean_class_acc,
valid_mean_iou_index,
test_global_acc,
test_mean_class_acc,
test_mean_iou_index) = best
print("")
print("Global Accuracies:")
print('Best: Train {:.5f} Valid {:.5f} Test {:.5f}'.format(
train_global_acc, valid_global_acc, test_global_acc))
print('Best: Mean Class Accuracy - Train {:.5f} Valid {:.5f} '
'Test {:.5f}'.format(train_mean_class_acc,
valid_mean_class_acc,
test_mean_class_acc))
print('Best: Mean Class iou - Train {:.5f} Valid {:.5f} '
'Test {:.5f}'.format(train_mean_iou_index,
valid_mean_iou_index,
test_mean_iou_index))
print("")
if len(saveto) != 1:
print("Moving temporary model files to {}".format(saveto[1]))
dirname = os.path.dirname(saveto[1])
if not os.path.exists(dirname):
os.makedirs(dirname)
move(saveto[0], saveto[1])
move(saveto[0] + '.pkl', saveto[1] + '.pkl')
end = time.time()
m, s = divmod(end - start, 60)
h, m = divmod(m, 60)
print("Total time elapsed: %d:%02d:%02d" % (h, m, s))
return best
def show_seg(dataset_name, n_exp, dataset_set, mode='sequential', id=-1):
"""
:param model_filename: model_recseg_namedataset1.npz
:param dataset_set: 'train', 'valid','test'
:param mode: 'random', 'sequential', 'filename', 'id'
:param id: 'filename' or 'index'
:return:
"""
# load options
model_filename = 'model_recseg_' + dataset_name + n_exp + ".npz"
try:
options = pkl.load(open(
os.path.expanduser(
os.path.join(dataset_name + "_models",
model_filename + '.pkl')), 'rb'))
saveto = options['saveto'][1]
except IOError:
pass
try:
options = pkl.load(open(
os.path.expanduser(
os.path.join("tmp",
model_filename + '.pkl')), 'rb'))
saveto = options['saveto'][0]
except IOError:
pass
if len(options) == 0:
print "Error file not found"
exit()
n_save = options['n_save']
n_save = -1
# Input Conv layers
in_nfilters = options['in_nfilters']
in_filters_size = options['in_filters_size']
in_filters_stride = options['in_filters_stride']
in_W_init = options['in_W_init']
in_b_init = options['in_b_init']
in_nonlinearity = options['in_nonlinearity']
# RNNs layers
dim_proj = options['dim_proj']
pwidth = options['pwidth']
pheight = options['pheight']
stack_sublayers = options['stack_sublayers']
RecurrentNet = options['RecurrentNet']
nonlinearity = options['nonlinearity']
hid_init = options['hid_init']
grad_clipping = options['grad_clipping']
precompute_input = options['precompute_input']
mask_input = options['mask_input']
# 1x1 Conv layer for dimensional reduction
conv_dim_red = options.get('conv_dim_red', None)
conv_dim_red_nonlinearity = options.get('conv_dim_red_nonlinearity', None)
# GRU specific params
gru_resetgate = options['gru_resetgate']
gru_updategate = options['gru_updategate']
gru_hidden_update = options['gru_hidden_update']
gru_hid_init = options['gru_hid_init']
# LSTM specific params
lstm_ingate = options['lstm_ingate']
lstm_forgetgate = options['lstm_forgetgate']
lstm_cell = options['lstm_cell']
lstm_outgate = options['lstm_outgate']
# RNN specific params
rnn_W_in_to_hid = options['rnn_W_in_to_hid']
rnn_W_hid_to_hid = options['rnn_W_hid_to_hid']
rnn_b = options['rnn_b']
# Output upsampling layers
out_upsampling = options['out_upsampling']
out_nfilters = options['out_nfilters']
out_filters_size = options['out_filters_size']
out_filters_stride = options['out_filters_stride']
out_W_init = options['out_W_init']
out_b_init = options['out_b_init']
out_nonlinearity = options['out_nonlinearity']
# Prediction, Softmax
class_balance = options['class_balance']
# Special layers
batch_norm = options['batch_norm']
valid_batch_size = options['valid_batch_size']
# Dataset
dataset = options['dataset']
color_space = options['color_space']
color = options['color']
use_depth = options.get('use_depth', None)
resize_images = options['resize_images']
resize_size = options['resize_size']
# Pre-processing
preprocess_type = options['preprocess_type']
patch_size = options['patch_size']
max_patches = options['max_patches']
# Save state from options
rng = options['rng']
# trng = options['trng'] --> to be reloaded after building the model
print_params(options)
n_layers = len(dim_proj)
assert class_balance in [None, 'median_freq_cost', 'natural_freq_cost',
'priors_correction'], (
'The balance class method is not implemented')
assert (preprocess_type in [None, 'f-whiten', 'conv-zca', 'sub-lcn',
'subdiv-lcn', 'gcn', 'local_mean_sub']), (
"The preprocessing method choosen is not implemented")
# Load data
# ---------
print("Loading data ...")
load_data, properties = get_dataset(dataset)
train, valid, test, mean, std, filenames, fullmasks = load_data(
resize_images=resize_images,
resize_size=resize_size,
color=color,
color_space=color_space,
rng=rng,
use_depth=use_depth,
with_filenames=True,
with_fullmasks=True)
has_void_class = properties()['has_void_class']
if not color:
if mean.ndim == 3:
mean = np.expand_dims(mean, axis=3)
if std.ndim == 3:
std = np.expand_dims(std, axis=3)
# Preprocess each image separately usually with LCN in order not to lose
# time at each epoch
# Default: input is float btw 0 and 1
# If we use vgg convnet the input should be 0:255
input_to_float = False if type(in_nfilters) == str else True
train, valid, test = preprocess_dataset(train, valid, test,
input_to_float,
preprocess_type,
patch_size, max_patches)
# Compute the indexes of the images to be saved
if isinstance(n_save, collections.Iterable):
samples_ids = np.array(n_save)
elif n_save != -1:
samples_ids = [
random.sample(range(len(s)), min(len(s), n_save)) for s in
[train[0], valid[0], test[0]]]
else:
samples_ids = [range(len(s)) for s in [train[0], valid[0], test[0]]]
options['samples_ids'] = samples_ids
# Retrieve basic size informations and split train variables
x_train, y_train = train
if len(x_train) == 0:
raise RuntimeError("Dataset not found")
filenames_train, filenames_valid, filenames_test = filenames
cheight, cwidth, cchannels = x_train[0].shape
nclasses = max([np.max(el) for el in y_train]) + 1
print '# of classes:', nclasses
# Remove the segmentation samples dir to make sure we don't mix samples
# from different experiments
seg_path = os.path.join('segmentations', dataset,
saveto.split('/')[-1][:-4])
# Class balancing
# ---------------
w_freq = 1
if class_balance in ['median_freq_cost', 'rare_freq_cost']:
# Get labels ids and number of pixels per label
u_train, c_train = np.unique(y_train, return_counts=True)
# The denominator is computed by summing the total number
# of pixels of the images where the class is present
px_count = np.zeros(u_train.shape)
for tt in y_train:
u_tt = np.unique(tt)
px_t = tt.size
for uu in u_tt:
px_count[uu] += px_t
priors = c_train.astype(theano.config.floatX) / px_count
if class_balance == 'median_freq_cost':
w_freq = np.median(priors) / priors
# we don't want to give more importance to the void class
if has_void_class:
w_freq[-1] = 0
elif class_balance == 'rare_freq_cost':
w_freq = 1 / (nclasses * priors)
print "Class balance weights", w_freq
assert len(priors) == nclasses, ("Number of computed priors are "
"different from number of classes")
try:
rmtree(seg_path)
except OSError:
pass
if dataset_set == 'train':
data = train
samples_ids = samples_ids[0]
filenames = filenames_train
elif dataset_set == 'valid':
data = valid
samples_ids = samples_ids[1]
filenames = filenames_valid
else:
data = test
samples_ids = samples_ids[2]
filenames = filenames_test
input_shape = (None, cheight, cwidth, cchannels)
input_var = T.tensor4('inputs')
l_out = buildReSeg(input_shape, input_var,
n_layers, pheight, pwidth,
dim_proj, nclasses, stack_sublayers,
# upsampling
out_upsampling,
out_nfilters,
out_filters_size,
out_filters_stride,
out_W_init=out_W_init,
out_b_init=out_b_init,
out_nonlinearity=out_nonlinearity,
# input ConvLayers
in_nfilters=in_nfilters,
in_filters_size=in_filters_size,
in_filters_stride=in_filters_stride,
in_W_init=in_W_init,
in_b_init=in_b_init,
in_nonlinearity=in_nonlinearity,
# common recurrent layer params
RecurrentNet=RecurrentNet,
nonlinearity=nonlinearity,
hid_init=hid_init,
grad_clipping=grad_clipping,
precompute_input=precompute_input,
mask_input=mask_input,
# 1x1 Conv layer for dimensional reduction
conv_dim_red=conv_dim_red,
conv_dim_red_nonlinearity=conv_dim_red_nonlinearity,
# GRU specific params
gru_resetgate=gru_resetgate,
gru_updategate=gru_updategate,
gru_hidden_update=gru_hidden_update,
gru_hid_init=gru_hid_init,
# LSTM specific params
lstm_ingate=lstm_ingate,
lstm_forgetgate=lstm_forgetgate,
lstm_cell=lstm_cell,
lstm_outgate=lstm_outgate,
# RNN specific params
rnn_W_in_to_hid=rnn_W_in_to_hid,
rnn_W_hid_to_hid=rnn_W_hid_to_hid,
rnn_b=rnn_b,
# special layers
batch_norm=batch_norm)
# load best params
print("Loading parameter best model ...")
with np.load(saveto) as f:
bestparams_val = [f['arr_%d' % i] for i in range(len(f.files))]
lasagne.layers.set_all_param_values(l_out, bestparams_val[1])
input_shape = input_var.shape
# Compute BN params for prediction
batch_norm_params = dict()
if batch_norm:
batch_norm_params.update(
dict(batch_norm_update_averages=False))
batch_norm_params.update(
dict(batch_norm_use_averages=True))
print("Building model ...")
# Model compilation
# -----------------
# computes the deterministic distribution over the labels, i.e. we
# disable the stochastic layers such as Dropout
prediction = lasagne.layers.get_output(l_out, deterministic=True,
**batch_norm_params)
f_pred = theano.function(
[input_var],
T.argmax(prediction, axis=1).reshape(
(-1, input_shape[1], input_shape[2])))
# compute prediction on the dataset or on the image that we specified
(test_global_acc,
test_conf_matrix,
test_mean_class_acc,
test_iou_index,
test_mean_iou_index) = validate(f_pred,
data,
valid_batch_size,
has_void_class,
preprocess_type,
nclasses,
samples_ids=samples_ids,
filenames=filenames,
folder_dataset=dataset_set,
dataset=dataset,
saveto=saveto[0])
print("")
print("Global Accuracies :")
print('Test ', test_global_acc)
print("")
print("Class Accuracies :")
print('Test ', test_mean_class_acc)
print("")
print("Mean Intersection Over Union :")
print('Test ', test_mean_iou_index)
print("")
if __name__ == '__main__':
if len(sys.argv) >= 3:
dataset_name = sys.argv[1]
n_exp = sys.argv[2]
else:
print "Usage: dataset_name n_exp, e.g. python reseg.py camvid 1"
sys.exit()
if len(sys.argv) > 3:
if sys.argv[3] in ['train', 'valid', 'test']:
dataset_set = sys.argv[3]
else:
print "Usage: choose one between 'train', 'valid', 'test'"
sys.exit()
else:
dataset_set = 'test'
if len(sys.argv) > 4:
if sys.argv[4] in ['random', 'sequential', 'filename', 'id']:
mode = sys.argv[4]
if mode in ['filename', 'id']:
if len(sys.argv) < 6:
print "Insert a correct filename or id!"
sys.exit()
else:
id = sys.argv[5]
else:
id = -1
else:
print "Usage: mode can be 'random', 'sequential', 'filename', 'id'"
sys.exit()
else:
mode = 'sequential'
show_seg(dataset_name, n_exp, dataset_set)
================================================
FILE: utils.py
================================================
from collections import OrderedDict
import os
import matplotlib
from matplotlib import cm, pyplot
import numpy as np
from progressbar import Bar, FormatLabel, Percentage, ProgressBar, Timer
from progressbar.widgets import FormatWidgetMixin, WidthWidgetMixin
from retrying import retry
from skimage import img_as_ubyte
from sklearn.metrics import confusion_matrix
from skimage.color import label2rgb, gray2rgb
from skimage.io import imsave
import theano
from config_datasets import colormap_datasets
floatX = theano.config.floatX
def iterate_minibatches(inputs, targets, batchsize, rng=None, shuffle=False):
'''Batch iterator
This is just a simple helper function iterating over training data in
mini-batches of a particular size, optionally in random order. It assumes
data is available as numpy arrays. For big datasets, you could load numpy
arrays as memory-mapped files (np.load(..., mmap_mode='r')), or write your
own custom data iteration function. For small datasets, you can also copy
them to GPU at once for slightly improved performance. This would involve
several changes in the main program, though, and is not demonstrated here.
'''
assert len(inputs) == len(targets)
if shuffle:
if rng is None:
raise Exception("A Numpy RandomState instance is needed!")
indices = np.arange(len(inputs))
rng.shuffle(indices)
for start_idx in range(0, len(inputs) - batchsize + 1, batchsize):
if shuffle:
excerpt = indices[start_idx:start_idx + batchsize]
else:
excerpt = slice(start_idx, start_idx + batchsize)
yield inputs[excerpt], targets[excerpt], excerpt
def save_image(outpath, img):
import errno
try:
os.makedirs(os.path.dirname(outpath))
except OSError as e:
if e.errno != errno.EEXIST:
raise e
pass
imsave(outpath, img_as_ubyte(img))
def validate(f_pred,
data,
batchsize,
has_void,
preprocess_type=None,
nclasses=2,
samples_ids=[],
dataset='camvid',
saveto='test_lasagne',
mean=None, std=None, fullmasks=None,
filenames=None, folder_dataset='pred'):
"""Validate the model
Returns
-------
The function returns the following performance indexes computed on the
input dataset:
* Global Pixel Accuracy
* Confusion Matrix
* Mean Class Accuracy (Mean of the diagonal of Norm Conf Matrix)
* Intersection Over Union Indexes for each class
* Intersection Over Union Index
"""
# check if the dataset is empty
if len(data) == 0 or len(samples_ids) == 0:
return 0., [], 0., [], 0.
seg_path = os.path.join('segmentations', dataset,
saveto.split('/')[-1][:-4])
try:
colormap = colormap_datasets[dataset]
except KeyError:
color_bins = np.linspace(0, 1, nclasses)
norm_bins = matplotlib.colors.Normalize(vmin=0, vmax=1)
m = cm.ScalarMappable(norm=norm_bins, cmap=pyplot.get_cmap('Pastel2'))
colormap = m.to_rgba(color_bins)[:, :3]
inputs, targets = data
conf_matrix = np.zeros([nclasses, nclasses]).astype('float32')
# Progressbar
n_imgs = inputs.shape[0]
bar_widgets = [
folder_dataset + ':', FormatLabel('%(value)d/' + str(n_imgs)), ' ',
Bar(marker='#'), ' ', Percentage(), ' ', Timer()]
pbar = ProgressBar(widgets=bar_widgets, maxval=n_imgs)
for i, minibatch in enumerate(iterate_minibatches(inputs,
targets,
batchsize,
shuffle=False)):
mini_x, mini_y, mini_slice = minibatch
# VGG needs 0:255 int inputs
#if preprocess_type is None:
# mini_x = img_as_float(mini_x)
mini_f = filenames[mini_slice]
preds = f_pred(mini_x.astype(floatX))
# just for visualization
if np.max(mini_x) > 1:
mini_x = (mini_x / 255.).astype(floatX)
# Compute the confusion matrix for each image
cf_m = confusion_matrix(mini_y.flatten(), preds.flatten(),
range(0, nclasses))
conf_matrix += cf_m
# Save samples
if len(samples_ids) > 0:
for pred, x, y, f in zip(preds, mini_x, mini_y, mini_f):
if i in samples_ids:
# Fix hdf5 stores string into an ndarray
if isinstance(f, np.ndarray) and len(f) == 1:
f = f[0]
# Do not use pgm as an extension
f = f.replace(".pgm", ".png")
# Handle RGB-D or grey_img + disparity
if x.shape[-1] in (1, 2):
x = gray2rgb(x[:, :, 0])
elif x.shape[-1] == 4:
x = x[:, :, :-1]
# Save Image + GT + prediction
im_name = os.path.basename(f)
pred_rgb = label2rgb(pred, colors=colormap)
y_rgb = label2rgb(y, colors=colormap)
im_save = np.concatenate((x, y_rgb, pred_rgb), axis=1)
outpath = os.path.join(seg_path, folder_dataset, im_name)
save_image(outpath, im_save)
pbar.update(min(i*batchsize + 1, n_imgs))
pbar.update(n_imgs) # always get to 100%
pbar.finish()
# Compute per class metrics
per_class_TP = np.diagonal(conf_matrix).astype(floatX)
per_class_FP = conf_matrix.sum(axis=0) - per_class_TP
per_class_FN = conf_matrix.sum(axis=1) - per_class_TP
# Compute global accuracy
n_pixels = np.sum(conf_matrix)
if has_void:
n_pixels -= np.sum(conf_matrix[-1, :])
global_acc = per_class_TP[:-1].sum() / float(n_pixels)
else:
global_acc = per_class_TP.sum() / float(n_pixels)
# Class Accuracy
class_acc = per_class_TP / (per_class_FN + per_class_TP)
class_acc = np.nan_to_num(class_acc)
mean_class_acc = (np.mean(class_acc[:-1]) if has_void else
np.mean(class_acc))
# Class Intersection over Union
iou_index = per_class_TP / (per_class_TP + per_class_FP + per_class_FN)
iou_index = np.nan_to_num(iou_index)
mean_iou_index = (np.mean(iou_index[:-1]) if has_void else
np.mean(iou_index))
return global_acc, conf_matrix, mean_class_acc, iou_index, mean_iou_index
def zipp(vparams, params):
"""Copy values from one dictionary to another.
It will copy all the values from the first dictionary to the second
dictionary.
Parameters
----------
vparams : dict
The dictionary to read the parameters from
params :
The dictionary to write the parameters to
"""
for kk, vv in vparams.iteritems():
params[kk].set_value(vv)
def unzip(zipped, prefix=None):
"""Return a dict of values out of a dict of theano variables
If a prefix is provided it will attach the prefix to the name of the
keys in the dictionary
Parameters
----------
zipped : dict
The dictionary of theano variables
prefix : string, optional
A prefix to be added to the keys of dictionary
"""
prefix = '' if prefix is None else prefix + '_'
new_params = OrderedDict()
for kk, vv in zipped.iteritems():
new_params[prefix + kk] = vv.get_value()
return new_params
def unroll(deep_list):
""" Unroll a deep list into a shallow list
Parameters
----------
deep_list : list or tuple
An annidated list of lists and/or tuples. Must not be empty.
Note
----
The list comprehension is equivalent to:
```
if type(deep_list) in [list, tuple] and len(deep_list):
if len(deep_list) == 1:
return unroll(deep_list[0])
else:
return unroll(deep_list[0]) + unroll(deep_list[1:])
else:
return [deep_list]
```
"""
return ((unroll(deep_list[0]) if len(deep_list) == 1 else
unroll(deep_list[0]) + unroll(deep_list[1:]))
if type(deep_list) in [list, tuple] and len(deep_list) else
[deep_list])
def retry_if_io_error(exception):
"""Return True if IOError.
Return True if we should retry (in this case when it's an IOError),
False otherwise.
"""
print "Filesystem error, retrying in 2 seconds..."
return isinstance(exception, IOError)
@retry(stop_max_attempt_number=10, wait_fixed=2000,
retry_on_exception=retry_if_io_error)
def save_with_retry(saveto, args):
if not os.path.exists(os.path.dirname(saveto)):
os.makedirs(os.path.dirname(saveto))
np.savez(saveto, *args)
def ceildiv(a, b):
"""Division rounded up
Parameters
----------
a : number
The numerator
b : number
The denominator
Reference
---------
http://stackoverflow.com/questions/14822184/is-there-a-ceiling-equivalent\
-of-operator-in-python
"""
return -(-a // b)
def to_float(l):
"""Converts an iterable in a list of floats
Parameters
----------
l : iterable
The iterable to be converted to float
"""
return [float(el) for el in l]
def to_int(l):
"""Converts an iterable in a list of ints
Parameters
----------
l : iterable
The iterable to be converted to float
"""
return [int(el) for el in l]
class VariableText(FormatWidgetMixin, WidthWidgetMixin):
mapping = {}
def __init__(self, format, mapping=mapping, **kwargs):
self.format = format
self.mapping = mapping
FormatWidgetMixin.__init__(self, format=format, **kwargs)
WidthWidgetMixin.__init__(self, **kwargs)
def update_str(self, new_format):
self.format = new_format
def update_mapping(self, new_mapping):
self.mapping.update(new_mapping)
def __call__(self, progress, data):
return FormatWidgetMixin.__call__(self, progress, self.mapping,
self.format)
================================================
FILE: vgg16.py
================================================
# VGG-16, 16-layer model from the paper:
# "Very Deep Convolutional Networks for Large-Scale Image Recognition"
# Original source: https://gist.github.com/ksimonyan/211839e770f7b538e2d8
# License: non-commercial use only
# Download pretrained weights from:
# https://s3.amazonaws.com/lasagne/recipes/pretrained/imagenet/vgg16.pkl
from collections import OrderedDict
import numpy
try:
import cPickle as pickle
except:
import pickle
import lasagne
import lasagne.layers
from lasagne.layers import (InputLayer, DenseLayer,
NonlinearityLayer, ConcatLayer)
from lasagne.nonlinearities import softmax
from padded import PaddedConv2DLayer
from padded import PaddedPool2DLayer
import theano
class Vgg16Layer(lasagne.layers.Layer):
def __init__(self,
l_in=InputLayer((None, 3, 224, 224)),
get_layer='prob',
padded=True,
trainable=False,
regularizable=False,
name='vgg'):
super(Vgg16Layer, self).__init__(l_in, name)
self.l_in = l_in
self.get_layer = get_layer
self.padded = padded
self.trainable = trainable
self.regularizable = regularizable
if padded:
ConvLayer = PaddedConv2DLayer
PoolLayer = PaddedPool2DLayer
else:
try:
ConvLayer = lasagne.layers.dnn.Conv2DDNNLayer
except AttributeError:
ConvLayer = lasagne.layers.Conv2DLayer
PoolLayer = lasagne.layers.Pool2DLayer
net = OrderedDict()
net['input'] = l_in
net['bgr'] = RGBtoBGRLayer(net['input'])
net['conv1_1'] = ConvLayer(
net['bgr'], 64, 3, pad=1, flip_filters=False)
net['conv1_2'] = ConvLayer(
net['conv1_1'], 64, 3, pad=1, flip_filters=False)
net['pool1'] = PoolLayer(
net['conv1_2'], 2)
net['conv2_1'] = ConvLayer(
net['pool1'], 128, 3, pad=1, flip_filters=False)
net['conv2_2'] = ConvLayer(
net['conv2_1'], 128, 3, pad=1, flip_filters=False)
net['pool2'] = PoolLayer(
net['conv2_2'], 2)
net['conv3_1'] = ConvLayer(
net['pool2'], 256, 3, pad=1, flip_filters=False)
net['conv3_2'] = ConvLayer(
net['conv3_1'], 256, 3, pad=1, flip_filters=False)
net['conv3_3'] = ConvLayer(
net['conv3_2'], 256, 3, pad=1, flip_filters=False)
net['pool3'] = PoolLayer(
net['conv3_3'], 2)
net['conv4_1'] = ConvLayer(
net['pool3'], 512, 3, pad=1, flip_filters=False)
net['conv4_2'] = ConvLayer(
net['conv4_1'], 512, 3, pad=1, flip_filters=False)
net['conv4_3'] = ConvLayer(
net['conv4_2'], 512, 3, pad=1, flip_filters=False)
net['pool4'] = PoolLayer(
net['conv4_3'], 2)
net['conv5_1'] = ConvLayer(
net['pool4'], 512, 3, pad=1, flip_filters=False)
net['conv5_2'] = ConvLayer(
net['conv5_1'], 512, 3, pad=1, flip_filters=False)
net['conv5_3'] = ConvLayer(
net['conv5_2'], 512, 3, pad=1, flip_filters=False)
net['pool5'] = PoolLayer(
net['conv5_3'], 2)
if 'fc' in get_layer or get_layer == 'prob':
net['fc6'] = DenseLayer(net['pool5'], num_units=4096)
net['fc7'] = DenseLayer(net['fc6'], num_units=4096)
net['fc8'] = DenseLayer(net['fc7'],
num_units=1000,
nonlinearity=None)
net['prob'] = NonlinearityLayer(net['fc8'], softmax)
self.concat_sublayers = []
if 'concat' in get_layer:
n_pool = get_layer[6:]
get_layer = 'pool' + str(n_pool)
l_concat = net['conv1_1']
for i in range(int(n_pool)):
l_conv = net['conv' + str(i+1) + '_1']
l_pool = net['pool' + str(i+1)]
l_new = ConvLayer(
l_concat, l_conv.num_filters, 2, pad=0, stride=2,
flip_filters=True,
name='vgg16_skipconnection_conv_' + str(i+1))
self.concat_sublayers.append(l_new)
l_concat = ConcatLayer(
(l_pool, l_new), axis=1,
name='vgg16_skipconnection_concat_' + str(i))
self.concat_sublayers.append(l_concat)
out_layer = l_concat
else:
out_layer = net[get_layer]
reached = False
# Collect garbage
for el in net.iteritems():
if reached:
del(net[el[0]])
if el[0] == get_layer:
reached = True
self.sublayers = net
# Set names to layers
for name in net.keys():
if not net[name].name:
net[name].name = 'vgg16_' + name
# Reload weights
nparams = len(lasagne.layers.get_all_params(net.values()))
with open('w_vgg16.pkl', 'rb') as f:
# Note: in python3 use the pickle.load parameter
# `encoding='latin-1'`
vgg16_w = pickle.load(f)['param values']
lasagne.layers.set_all_param_values(net.values(), vgg16_w[:nparams])
# Do not train or regularize vgg
if not trainable or not regularizable:
all_layers = net.values()
for vgg_layer in all_layers:
if 'concat' not in vgg_layer.name:
layer_params = vgg_layer.get_params()
for p in layer_params:
if not regularizable:
try:
vgg_layer.params[p].remove('regularizable')
except KeyError:
pass
if not trainable:
try:
vgg_layer.params[p].remove('trainable')
except KeyError:
pass
# save the vgg sublayers
self.out_layer = out_layer
# HACK LASAGNE
# This will set `self.input_layer`, which is needed by Lasagne to find
# the layers with the get_all_layers() helper function in the
# case of a layer with sublayers
if isinstance(self.out_layer, tuple):
self.input_layer = None
else:
self.input_layer = self.out_layer
def get_output_for(self, input_var, **kwargs):
# HACK LASAGNE
# This is needed, jointly with the previous hack, to ensure that
# this layer behaves as its last sublayer (namely,
# self.input_layer)
return input_var
def get_output_shape_for(self, input_shape):
c_input_shape = input_shape
# iterate through vgg
for name, layer in self.sublayers.items()[1:]:
output_shape = layer.get_output_shape_for(input_shape)
input_shape = output_shape
# iterate through the parallel network if any
for layer in self.concat_sublayers:
if isinstance(layer, ConcatLayer):
c_input_shape = (c_input_shape, c_input_shape)
output_shape = layer.get_output_shape_for(c_input_shape)
c_input_shape = output_shape
return output_shape
class RGBtoBGRLayer(lasagne.layers.Layer):
def __init__(self, l_in, bgr_mean=numpy.array([103.939, 116.779, 123.68]),
data_format='bc01', **kwargs):
"""A Layer to normalize and convert images from RGB to BGR
This layer converts images from RGB to BGR to adapt to Caffe
that uses OpenCV, which uses BGR. It also subtracts the
per-pixel mean.
Parameters
----------
l_in : :class:``lasagne.layers.Layer``
The incoming layer, typically an
:class:``lasagne.layers.InputLayer``
bgr_mean : iterable of 3 ints
The mean of each channel. By default, the ImageNet
mean values are used.
data_format : str
The format of l_in, either `b01c` (batch, rows, cols,
channels) or `bc01` (batch, channels, rows, cols)
"""
super(RGBtoBGRLayer, self).__init__(l_in, **kwargs)
assert data_format in ['bc01', 'b01c']
self.l_in = l_in
floatX = theano.config.floatX
self.bgr_mean = bgr_mean.astype(floatX)
self.data_format = data_format
def get_output_for(self, input_im, **kwargs):
if self.data_format == 'bc01':
input_im = input_im[:, ::-1, :, :]
input_im -= self.bgr_mean[:, numpy.newaxis, numpy.newaxis]
else:
input_im = input_im[:, :, :, ::-1]
input_im -= self.bgr_mean
return input_im